diff --git a/.github/actions/python-uv-setup/action.yml b/.github/actions/python-uv-setup/action.yml
new file mode 100644
index 00000000..6ac9145d
--- /dev/null
+++ b/.github/actions/python-uv-setup/action.yml
@@ -0,0 +1,74 @@
+name: "python-uv-setup"
+description: "Common Python + uv setup, deps install, and optional debug"
+
+inputs:
+  python-version:
+    description: "Python version"
+    required: true
+  debug:
+    description: "Enable debug output"
+    required: false
+    default: "false"
+  lockfile:
+    description: "Lockfile to use"
+    required: false
+    default: "uv.lock.cpu"
+  extras:
+    description: "Extra dependency set to install"
+    required: false
+    default: "cpu"
+  dependency-groups:
+    description: "Dependency group to install"
+    required: false
+    default: "dev"
+
+runs:
+  using: "composite"
+  steps:
+    - name: System Dependencies
+      shell: bash
+      run: |
+        sudo apt-get update
+        sudo apt-get install -y \
+          build-essential \
+          libcairo2-dev \
+          pkg-config \
+          python3-dev \
+          python3-openssl \
+          libopenblas-dev \
+          libssl-dev
+
+    - name: Set up Python
+      uses: actions/setup-python@v5
+      with:
+        python-version: ${{ inputs.python-version }}
+
+    - name: Install uv
+      shell: bash
+      run: |
+        python -m pip install --upgrade uv
+        uv --version
+
+    - name: Cache uv dependencies
+      uses: actions/cache@v4
+      with:
+        path: ~/.cache/uv
+        key: ${{ runner.os }}-py${{ inputs.python-version }}-uv-${{ hashFiles('pyproject.toml', 'uv.lock.cpu') }}
+        restore-keys: |
+          ${{ runner.os }}-py${{ inputs.python-version }}-uv-
+
+    - name: Install dependencies
+      shell: bash
+      run: |
+        set -euxo pipefail
+        cp "${{ inputs.lockfile }}" uv.lock
+        uv venv .venv
+        . .venv/bin/activate
+        uv sync --extra "${{ inputs.extras }}" --group "${{ inputs.dependency-groups }}" --locked
+
+    - name: Debug - what is installed
+      if: ${{ inputs.debug == 'true' }}
+      shell: bash
+      run: |
+        . .venv/bin/activate
+        uv pip list
diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index c7d31cb9..ff978b62 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -1,59 +1,64 @@
+# How to run manually:
+# 1. Go to the GitHub repository.
+# 2. Click the "Actions" tab.
+# 3. Select this workflow, "docker publish".
+# 4. Click "Run workflow".
+# 5. Choose the desired inputs, then click "Run workflow".
+
 name: docker publish
 
 on:
-  release:
-    types: [published]
+  workflow_dispatch:
+    inputs:
+      env:
+        description: "Deployment environment"
+        required: true
+        default: "prod"
+        type: choice
+        options:
+          - prod
+          - staging
+      target:
+        description: "Which image(s) to build"
+        required: true
+        default: "all"
+        type: choice
+        options:
+          - all
+          - gpu
+          - rocm
+          - cpu-amd64
+          - cpu-arm64
 
 jobs:
-  build-gpu-image:
+  build-and-push:
     runs-on: ubuntu-24.04
-    steps:
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
-
-      - name: Log in to DockerHub
-        uses: docker/login-action@v3
-        with:
-          username: ${{ secrets.DOCKER_USERNAME }}
-          password: ${{ secrets.DOCKER_PASSWORD }}
-
-      - name: Checkout code
-        uses: actions/checkout@v3
-        with:
-          fetch-depth: 1
+    strategy:
+      fail-fast: false
+      matrix:
+        include:
+          # Uses Dockerfile.gpu
+          - name: gpu
+            platform: gpu
+            arch: ""
+          # Uses Dockerfile.rocm
+          - name: rocm
+            platform: rocm
+            arch: ""
+          # Uses Dockerfile.cpu
+          - name: cpu-amd64
+            platform: cpu
+            arch: ""
+          # Uses Dockerfile.arm
+          - name: cpu-arm64
+            platform: arm
+            arch: linux/arm64
 
-      - name: Build and push Docker image (gpu)
-        run: |
-          make push-docker
-        env:
-          ENV: prod
-          PLATFORM: gpu
-  build-cpu-linux-amd64:
-    runs-on: ubuntu-24.04
     steps:
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
-
-      - name: Log in to DockerHub
-        uses: docker/login-action@v3
-        with:
-          username: ${{ secrets.DOCKER_USERNAME }}
-          password: ${{ secrets.DOCKER_PASSWORD }}
+      - name: Set up QEMU
+        if: ${{ matrix.arch == 'linux/arm64' }}
+        uses: docker/setup-qemu-action@v3
 
-      - name: Checkout code
-        uses: actions/checkout@v3
-        with:
-          fetch-depth: 1
-
-      - name: Build and push Docker image (cpu)
-        run: |
-          make push-docker
-        env:
-          ENV: prod
-          PLATFORM: cpu
-  build-cpu-linux-arm64:
-    runs-on: ubuntu-24.04
-    steps:
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@v3
 
@@ -64,14 +69,14 @@ jobs:
           password: ${{ secrets.DOCKER_PASSWORD }}
 
       - name: Checkout code
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
         with:
           fetch-depth: 1
 
-      - name: Build and push Docker image (cpu)
-        run: |
-          make push-docker
+      - name: Build and push Docker image
+        if: ${{ github.event.inputs.target == 'all' || github.event.inputs.target == matrix.name }}
+        run: make push-docker
         env:
-          ARCH: linux/arm64
-          ENV: prod
-          PLATFORM: arm
+          ENV: ${{ github.event.inputs.env }}
+          PLATFORM: ${{ matrix.platform }}
+          ARCH: ${{ matrix.arch }}
diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml
index 0be18942..009e3f7d 100644
--- a/.github/workflows/lint.yml
+++ b/.github/workflows/lint.yml
@@ -7,9 +7,13 @@ jobs:
     runs-on: ubuntu-24.04
     strategy:
       matrix:
-        python-version: ["3.9"]
+        python-version: ["3.10"]
 
     steps:
+      - name: Update apt cache
+        run: |
+          sudo apt-get update || sudo apt-get update --fix-missing
+
       - name: System Dependencies
         run: |
           sudo apt install -y build-essential \
@@ -18,30 +22,38 @@ jobs:
             python3-dev \
             python3-openssl
       - uses: actions/checkout@v3
+
       - name: Set up Python ${{ matrix.python-version }}
         uses: actions/setup-python@v4
         with:
           python-version: ${{ matrix.python-version }}
 
-      - name: Cache Python dependencies
+      - name: Install uv
+        run: |
+          python -m pip install --upgrade uv
+          uv --version
+
+      - name: Cache uv downloads
         uses: actions/cache@v3
         with:
-          path: ~/.cache/pip
-          key: ${{ runner.os }}-pip-${{ hashFiles('pip/*.txt') }}
+          path: ~/.cache/uv
+          key: ${{ runner.os }}-uv-${{ hashFiles('pip/*.txt') }}
           restore-keys: |
-            ${{ runner.os }}-pip-
+            ${{ runner.os }}-uv-
 
-      - name: Install dependencies
+      - name: Create venv
         run: |
-          python3.9 -m pip install --upgrade pip
-          if [ -f pip/cpu_requirements.txt ]; then pip install -r pip/cpu_requirements.txt; fi
+          uv venv .venv
 
-      - name: Install ruff
+      - name: Install dependencies
         run: |
-          python3.9 -m pip install ruff
+          . .venv/bin/activate
+          if [ -f pip/cpu_requirements.txt ]; then pip install -r pip/cpu_requirements.txt; fi
+          uv pip install ruff
 
       - name: Lint with ruff
         run: |
           # Check all lint rules
-          python3.9 -m ruff check atomsci/
+          . .venv/bin/activate
+          uv run ruff check atomsci/
 
diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml
new file mode 100644
index 00000000..a4f90e24
--- /dev/null
+++ b/.github/workflows/publish.yml
@@ -0,0 +1,131 @@
+name: Publish Python Package
+
+# Publish atomsci-ampl to TestPyPI and PyPI
+#
+# Owner           -> ATOMScience-org
+# Repository name -> AMPL
+# Project         -> atomsci-ampl
+#
+# This workflow is manual only.
+#
+# Workflow behavior:
+#
+# 1. Build the package distribution
+# 2. Optionally publish to TestPyPI
+# 3. Optionally publish to PyPI
+#
+# How to use:
+#
+# - Run this workflow manually from the GitHub Actions tab
+# - Choose whether to publish to TestPyPI
+# - Choose whether to publish to PyPI
+#
+# Notes:
+#
+# - Package versions cannot be reused once published
+# - For TestPyPI testing, use development version names such as:
+#
+#     1.2.3.dev1
+#
+# - If you want PyPI publication to require approval, configure
+#   protection rules on the "pypi" environment
+#
+# Recommended usage:
+#
+# - TestPyPI only:
+#     publish_testpypi = true
+#     publish_pypi     = false
+#
+# - TestPyPI and PyPI:
+#     publish_testpypi = true
+#     publish_pypi     = true
+
+on:
+  workflow_dispatch:
+    inputs:
+      publish_testpypi:
+        description: "Publish to TestPyPI"
+        required: true
+        type: boolean
+        default: true
+      publish_pypi:
+        description: "Publish to PyPI"
+        required: true
+        type: boolean
+        default: false
+
+permissions:
+  contents: read
+
+jobs:
+  # Job 1: Build the source distribution and wheel
+  build:
+    name: Build package
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Check out repository
+        uses: actions/checkout@v4
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.x"
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v4
+
+      - name: Build sdist and wheel
+        run: uv build
+
+      - name: Upload dist artifact
+        uses: actions/upload-artifact@v4
+        with:
+          name: dist
+          path: dist/
+          retention-days: 7
+
+  # Job 2: Optionally publish to TestPyPI
+  publish-test:
+    name: Publish to TestPyPI
+    runs-on: ubuntu-latest
+    needs: build
+    if: ${{ inputs.publish_testpypi }}
+    environment: testpypi
+
+    steps:
+      - name: Download dist artifact
+        uses: actions/download-artifact@v4
+        with:
+          name: dist
+          path: dist/
+
+      - name: Publish to TestPyPI
+        uses: pypa/gh-action-pypi-publish@release/v1
+        with:
+          repository-url: https://test.pypi.org/legacy/
+          user: __token__
+          password: ${{ secrets.TEST_PYPI_API_TOKEN }}
+
+  # Job 3: Optionally publish to PyPI
+  # This runs only if PyPI publishing was selected.
+  # It also depends on the TestPyPI job, so TestPyPI must be selected too.
+  publish-pypi:
+    name: Publish to PyPI
+    runs-on: ubuntu-latest
+    needs: publish-test
+    if: ${{ inputs.publish_testpypi && inputs.publish_pypi }}
+    environment: pypi
+
+    steps:
+      - name: Download dist artifact
+        uses: actions/download-artifact@v4
+        with:
+          name: dist
+          path: dist/
+
+      - name: Publish to PyPI
+        uses: pypa/gh-action-pypi-publish@release/v1
+        with:
+          user: __token__
+          password: ${{ secrets.PYPI_API_TOKEN }}
diff --git a/.github/workflows/pytest.yml b/.github/workflows/pytest.yml
index 6ec0bef5..e372a67d 100644
--- a/.github/workflows/pytest.yml
+++ b/.github/workflows/pytest.yml
@@ -1,242 +1,151 @@
 name: tests
 
-on: 
-    push: 
+on:
+    push:
       branches: ["**"]
     pull_request:
-      types:
-        - opened
-        - reopened
-        - ready_for_review
+      types: [opened, reopened, ready_for_review]
 
 jobs:
   pytest-unit:
     runs-on: ubuntu-24.04
+    env:
+      ENV: test
+      DGLBACKEND: pytorch
     strategy:
       matrix:
-        python-version: ["3.9"]
+        python-version: ["3.10"]
     steps:
-      - name: System Dependencies
-        run: |
-          sudo apt install -y build-essential \
-            libcairo2-dev \
-            pkg-config \
-            python3-dev \
-            python3-openssl
-      - uses: actions/checkout@v3
-      - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v4
+      - uses: actions/checkout@v4
+      - uses: ./.github/actions/python-uv-setup
         with:
           python-version: ${{ matrix.python-version }}
-
-      - name: Cache Python dependencies
-        uses: actions/cache@v3
-        with:
-          path: ~/.cache/pip
-          key: ${{ runner.os }}-pip-${{ hashFiles('pip/*.txt') }}
-          restore-keys: |
-            ${{ runner.os }}-pip-
-
-      - name: Install dependencies
+          lockfile: uv.lock.cpu
+          extras: cpu
+          dependency-groups: dev
+          debug: "false"
+
+      - name: Run modac unit tests
+        shell: bash
+        working-directory: atomsci/modac/test/unit
         run: |
-          python3.9 -m pip install --upgrade pip
-          pip install -e .
-          if [ -f pip/cpu_requirements.txt ]; then pip install -r pip/cpu_requirements.txt; fi
-          if [ -f pip/dev_requirements.txt ]; then pip install -r pip/dev_requirements.txt; fi
+          . "${{ github.workspace }}/.venv/bin/activate"
+          python -m pytest --capture=sys --capture=fd --cov=atomsci/ -vv
 
-      - name: pytest
+      - name: Run ddm unit tests
+        shell: bash
+        working-directory: atomsci/ddm/test/unit
         run: |
-          # python3.9 -m pytest --capture=sys --capture=fd --cov=atomsci/ -vv atomsci/ddm/test/unit
-          python3.9 -m pytest --capture=sys --capture=fd --cov=atomsci/ -vv atomsci/modac/test/unit
-          cd atomsci/ddm/test/unit && python3.9 -m pytest -n 2 --capture=sys --capture=fd --cov=atomsci -vv
-        env:
-          ENV: test
+          . "${{ github.workspace }}/.venv/bin/activate"
+          python -m pytest -m "not moe_required" -n 2 --capture=fd --cov=atomsci -vv --durations=4 --durations-min=0.05
 
-      - name: Upload coverage reports to Codecov
-        uses: codecov/codecov-action@v4
-        env:
-          CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
-  pytest-integrative-1:
+      - name: Save coverage
+        uses: actions/upload-artifact@v4
+        with:
+          name: coverage-pytest-unit
+          path: |
+            atomsci/ddm/test/unit/.coverage*
+            atomsci/modac/test/unit/.coverage*
+          include-hidden-files: true
+          if-no-files-found: error
+
+  pytest-integrative:
     runs-on: ubuntu-24.04
+    env:
+      ENV: test
+      DGLBACKEND: pytorch
+      DEBUG_DGL: "false"
     strategy:
+      fail-fast: false
       matrix:
-        python-version: ["3.9"]
+        python-version: ["3.10"]
+        chunk: [0, 1, 2, 3]
+
     steps:
-      - name: System Dependencies
-        run: |
-          sudo apt install -y build-essential \
-            libcairo2-dev \
-            pkg-config \
-            python3-dev \
-            python3-openssl
-      - uses: actions/checkout@v3
-      - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v4
+      - uses: actions/checkout@v4
+      - uses: ./.github/actions/python-uv-setup
         with:
           python-version: ${{ matrix.python-version }}
-
-      - name: Cache Python dependencies
-        uses: actions/cache@v3
-        with:
-          path: ~/.cache/pip
-          key: ${{ runner.os }}-pip-${{ hashFiles('pip/*.txt') }}
-          restore-keys: |
-            ${{ runner.os }}-pip-
-
-      - name: Install dependencies
+          lockfile: uv.lock.cpu
+          extras: cpu
+          dependency-groups: dev
+          debug: "false"
+
+      - name: Debug - dgl + torchdata + shadowing
+        shell: bash
+        if: ${{ env.DEBUG_DGL == 'true' }}
         run: |
-          python3.9 -m pip install --upgrade pip
-          pip install -e .
-          if [ -f pip/cpu_requirements.txt ]; then pip install -r pip/cpu_requirements.txt; fi
-          if [ -f pip/dev_requirements.txt ]; then pip install -r pip/dev_requirements.txt; fi
-
-      - name: pytest
+          . "${{ github.workspace }}/.venv/bin/activate"
+          set -x
+
+          which python
+          python -V
+          python -c "import sys, os; print('sys.executable:', sys.executable); print('sys.path[0]:', sys.path[0]); print('cwd:', os.getcwd())"
+          python -c "import importlib.util; spec = importlib.util.find_spec('torchdata'); print('torchdata spec:', spec); print('torchdata origin:', getattr(spec,'origin',None)); print('torchdata submodule locations:', getattr(spec,'submodule_search_locations',None))"
+          python -c "import torchdata; print('torchdata file:', torchdata.__file__); print('torchdata version:', getattr(torchdata,'__version__','no __version__'))"
+          python -c "import torchdata.datapipes.iter as it; print('torchdata.datapipes.iter OK:', it)"
+          python -c "import dgl; print('dgl OK:', dgl.__version__)"
+          python -m pip --version
+          python -m pip show torch torchdata dgl dgllife || true
+          python -m pip freeze | grep -E '^(torch|torchdata|dgl|dgllife|numpy)=' || true
+
+      - name: Run integrative tests
+        shell: bash
+        working-directory: atomsci/ddm/test/integrative
         run: |
-          # TODO: Run this test with pytest for paralell testing
-          # python3.9 -m pytest -n 2 --capture=sys --capture=fd --cov=atomsci -vv atomsci/ddm/test/integrative
-          cd atomsci/ddm/test/integrative && ./integrative_batch_chunk_tests 0
-        env:
-          ENV: test
-
-      - name: Upload coverage reports to Codecov
-        uses: codecov/codecov-action@v4
-        env:
-          CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
-  pytest-integrative-2:
-    runs-on: ubuntu-24.04
-    strategy:
-      matrix:
-        python-version: ["3.9"]
-    steps:
-      - name: System Dependencies
-        run: |
-          sudo apt install -y build-essential \
-            libcairo2-dev \
-            pkg-config \
-            python3-dev \
-            python3-openssl
-      - uses: actions/checkout@v3
-      - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v4
-        with:
-          python-version: ${{ matrix.python-version }}
+          . "${{ github.workspace }}/.venv/bin/activate"
+          ./integrative_batch_chunk_tests ${{ matrix.chunk }}
 
-      - name: Cache Python dependencies
-        uses: actions/cache@v3
+      - name: Save coverage
+        uses: actions/upload-artifact@v4
         with:
-          path: ~/.cache/pip
-          key: ${{ runner.os }}-pip-${{ hashFiles('pip/*.txt') }}
-          restore-keys: |
-            ${{ runner.os }}-pip-
-
-      - name: Install dependencies
-        run: |
-          python3.9 -m pip install --upgrade pip
-          pip install -e .
-          if [ -f pip/cpu_requirements.txt ]; then pip install -r pip/cpu_requirements.txt; fi
-          if [ -f pip/dev_requirements.txt ]; then pip install -r pip/dev_requirements.txt; fi
+          name: coverage-pytest-integrative-${{ matrix.chunk }}
+          path: atomsci/ddm/test/integrative/**/.coverage*
+          include-hidden-files: true
+          if-no-files-found: error
 
-      - name: pytest
-        run: |
-          # TODO: Run this test with pytest for paralell testing
-          # python3.9 -m pytest -n 2 --capture=sys --capture=fd --cov=atomsci -vv atomsci/ddm/test/integrative
-          cd atomsci/ddm/test/integrative && ./integrative_batch_chunk_tests 1
-        env:
-          ENV: test
-
-      - name: Upload coverage reports to Codecov
-        uses: codecov/codecov-action@v4
-        env:
-          CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
-  pytest-integrative-3:
+  coverage-merge:
     runs-on: ubuntu-24.04
-    strategy:
-      matrix:
-        python-version: ["3.9"]
+    needs: [pytest-unit, pytest-integrative]
     steps:
-      - name: System Dependencies
-        run: |
-          sudo apt install -y build-essential \
-            libcairo2-dev \
-            pkg-config \
-            python3-dev \
-            python3-openssl
-      - uses: actions/checkout@v3
-      - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v4
+      - uses: actions/checkout@v4
         with:
-          python-version: ${{ matrix.python-version }}
+          fetch-depth: 1
 
-      - name: Cache Python dependencies
-        uses: actions/cache@v3
+      - uses: actions/setup-python@v5
         with:
-          path: ~/.cache/pip
-          key: ${{ runner.os }}-pip-${{ hashFiles('pip/*.txt') }}
-          restore-keys: |
-            ${{ runner.os }}-pip-
-
-      - name: Install dependencies
-        run: |
-          python3.9 -m pip install --upgrade pip
-          pip install -e .
-          if [ -f pip/cpu_requirements.txt ]; then pip install -r pip/cpu_requirements.txt; fi
-          if [ -f pip/dev_requirements.txt ]; then pip install -r pip/dev_requirements.txt; fi
-
-      - name: pytest
-        run: |
-          # TODO: Run this test with pytest for paralell testing
-          # python3.9 -m pytest -n 2 --capture=sys --capture=fd --cov=atomsci -vv atomsci/ddm/test/integrative
-          cd atomsci/ddm/test/integrative && ./integrative_batch_chunk_tests 2
-        env:
-          ENV: test
+          python-version: "3.10"
 
-      - name: Upload coverage reports to Codecov
-        uses: codecov/codecov-action@v4
-        env:
-          CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
-  pytest-integrative-4:
-    runs-on: ubuntu-24.04
-    strategy:
-      matrix:
-        python-version: ["3.9"]
-    steps:
-      - name: System Dependencies
+      - name: Install coverage
+        shell: bash
         run: |
-          sudo apt install -y build-essential \
-            libcairo2-dev \
-            pkg-config \
-            python3-dev \
-            python3-openssl
-      - uses: actions/checkout@v3
-      - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v4
-        with:
-          python-version: ${{ matrix.python-version }}
+          python -m pip install --upgrade pip
+          python -m pip install coverage
 
-      - name: Cache Python dependencies
-        uses: actions/cache@v3
+      - name: Download all coverage artifacts
+        uses: actions/download-artifact@v5
         with:
-          path: ~/.cache/pip
-          key: ${{ runner.os }}-pip-${{ hashFiles('pip/*.txt') }}
-          restore-keys: |
-            ${{ runner.os }}-pip-
+          path: coverage-reports
 
-      - name: Install dependencies
+      - name: Merge coverage reports
         run: |
-          python3.9 -m pip install --upgrade pip
-          pip install -e .
-          if [ -f pip/cpu_requirements.txt ]; then pip install -r pip/cpu_requirements.txt; fi
-          if [ -f pip/dev_requirements.txt ]; then pip install -r pip/dev_requirements.txt; fi
+          # List directory structure for debugging
+          set -euo pipefail
+          ls -la coverage-reports || true
 
-      - name: pytest
-        run: |
-          # TODO: Run this test with pytest for paralell testing
-          # python3.9 -m pytest -n 2 --capture=sys --capture=fd --cov=atomsci -vv atomsci/ddm/test/integrative
-          cd atomsci/ddm/test/integrative && ./integrative_batch_chunk_tests 3
-        env:
-          ENV: test
+          # Find and combine all coverage files
+          find coverage-reports -name ".coverage*" -type f -print
+
+          # Combine all coverage files
+          find coverage-reports -name ".coverage*" -type f -print0 | xargs -0 coverage combine
+          # Generate XML report for codecov
+          coverage xml
 
-      - name: Upload coverage reports to Codecov
+      - name: Upload merged coverage to Codecov
         uses: codecov/codecov-action@v4
+        with:
+          files: ./coverage.xml
+          flags: unittests
+          name: codecov-umbrella
         env:
           CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
diff --git a/.gitignore b/.gitignore
index 976e80b8..e70d13d7 100644
--- a/.gitignore
+++ b/.gitignore
@@ -123,6 +123,8 @@ celerybeat.pid
 # Environments
 .env
 .venv
+.venv-*
+uv.lock
 env/
 venv/
 ENV/
diff --git a/.readthedocs.yml b/.readthedocs.yml
index 6c7fb338..73139e0f 100644
--- a/.readthedocs.yml
+++ b/.readthedocs.yml
@@ -1,14 +1,21 @@
 # .readthedocs.yml
 # Read the Docs configuration file
 # See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
+#
 
-# Required
-version: 1
+version: 2
+
+build:
+  os: ubuntu-22.04
+  tools:
+    python: "3.10"
 
-# Build documentation in the docs/ directory with Sphinx
 sphinx:
   configuration: docs/source/conf.py
 
-# Optionally set the version of Python and requirements required to build your docs
 python:
-   version: 3.7
+  install:
+    - method: pip
+      path: .
+      extra_requirements:
+        - docs
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index e608b538..da95a870 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -50,7 +50,19 @@ git fetch upstream
 git rebase upstream/master
 ```
 
-2. Set up a new environment for AMPL by following these [instructions](https://github.com/ATOMScience-org/AMPL#install).
+2. Set up a new environment for AMPL by following these [instructions](https://github.com/ATOMScience-org/AMPL#installation).
+
+This project uses [`uv`](https://docs.astral.sh/uv/) (a fast Python package and project manager) together with `pyproject.toml` and `uv.lock` for dependency management.
+
+### Prerequisites
+- Install **uv** (recommended way):
+  ```bash
+  # On macOS / Linux
+  curl -LsSf https://astral.sh/uv/install.sh | sh
+
+  # On Windows (PowerShell)
+  powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
+  ```
 
 Keep in mind, every contribution must pass the unit tests.
 
diff --git a/Dockerfile.arm b/Dockerfile.arm
index 7617ff21..65476b23 100644
--- a/Dockerfile.arm
+++ b/Dockerfile.arm
@@ -1,49 +1,20 @@
-FROM python:3.9
+FROM --platform=linux/arm64 python:3.10-slim
 
-# Prevent interactive dialogs from halting the build
-ENV DEBIAN_FRONTEND=noninteractive
-
-RUN apt update -y && apt install -y \
+RUN apt-get update && apt-get install -y \
     build-essential \
-    libcairo2-dev \
-    libhdf5-dev \
-    pkg-config \
-    python3-dev \
-    python3-openssl && \
-    apt clean && \
-    rm -rf /var/lib/apt/lists/*
+    git \
+    curl \
+    ca-certificates \
+    && rm -rf /var/lib/apt/lists/*
 
-COPY ./pip/mchip_requirements.txt ./pip/mchip_requirements.txt
-RUN pip install -U pip && \
-    pip install --no-cache-dir \
-    --trusted-host pypi.python.org \
-    --trusted-host files.pythonhosted.org \
-    --trusted-host pypi.org \
-    --trusted-host data.dgl.ai \
-    --trusted-host download.pytorch.org \
-    -r ./pip/mchip_requirements.txt
+RUN pip install --no-cache-dir uv
 
-COPY ./pip/mchip_dev_requirements.txt ./pip/mchip_dev_requirements.txt
-RUN pip install -U pip && \
-    pip install --no-cache-dir \
-    --trusted-host pypi.python.org \
-    --trusted-host files.pythonhosted.org \
-    --trusted-host pypi.org \
-    --trusted-host data.dgl.ai \
-    --trusted-host download.pytorch.org \
-    -r ./pip/mchip_dev_requirements.txt
+WORKDIR /app
 
-COPY setup.py .
+COPY pyproject.toml .
 COPY VERSION .
-COPY build.sh .
-COPY ./atomsci atomsci/
+COPY atomsci ./atomsci
 
-# Install atomsci-ampl
-RUN ./build.sh
-RUN pip install -e .
+RUN uv sync --extra mchip --group docker --no-dev
 
-# Clean up
-RUN apt purge -y build-essential python3-dev && \
-    apt autoremove -y && \
-    rm -rf /var/lib/apt/lists/*
-RUN rm -rf /root/.cache/pip
+CMD ["uv", "run", "python", "-c", "import atomsci; print('arm64/mchip-style ok')"]
diff --git a/Dockerfile.cpu b/Dockerfile.cpu
index 23b9223d..100ee8e3 100644
--- a/Dockerfile.cpu
+++ b/Dockerfile.cpu
@@ -1,49 +1,12 @@
-FROM python:3.9
+FROM python:3.10-slim
 
-# Prevent interactive dialogs from halting the build
-ENV DEBIAN_FRONTEND=noninteractive
+RUN pip install uv
+WORKDIR /app
 
-RUN apt update -y && apt install -y \
-    build-essential \
-    libcairo2-dev \
-    libhdf5-dev \
-    pkg-config \
-    python3-dev \
-    python3-openssl && \
-    apt clean && \
-    rm -rf /var/lib/apt/lists/*
-
-COPY ./pip/cpu_requirements.txt ./pip/cpu_requirements.txt
-RUN pip install -U pip && \
-    pip install --no-cache-dir \
-    --trusted-host pypi.python.org \
-    --trusted-host files.pythonhosted.org \
-    --trusted-host pypi.org \
-    --trusted-host data.dgl.ai \
-    --trusted-host download.pytorch.org \
-    -r ./pip/cpu_requirements.txt
-
-COPY ./pip/dev_requirements.txt ./pip/dev_requirements.txt
-RUN pip install -U pip && \
-    pip install --no-cache-dir \
-    --trusted-host pypi.python.org \
-    --trusted-host files.pythonhosted.org \
-    --trusted-host pypi.org \
-    --trusted-host data.dgl.ai \
-    --trusted-host download.pytorch.org \
-    -r ./pip/dev_requirements.txt
-
-COPY setup.py .
+COPY pyproject.toml .
+COPY atomsci ./atomsci
 COPY VERSION .
-COPY build.sh .
-COPY ./atomsci atomsci/
 
-# Install atomsci-ampl
-RUN ./build.sh
-RUN pip install -e .
+RUN uv sync --extra cpu
 
-# Clean up
-RUN apt purge -y build-essential python3-dev && \
-    apt autoremove -y && \
-    rm -rf /var/lib/apt/lists/*
-RUN rm -rf /root/.cache/pip
+CMD ["uv", "run", "python", "-c", "import atomsci; print('cpu ok')"]
diff --git a/Dockerfile.gpu b/Dockerfile.gpu
index 62ff8b86..88ed0553 100644
--- a/Dockerfile.gpu
+++ b/Dockerfile.gpu
@@ -1,49 +1,13 @@
-FROM python:3.9
+FROM nvidia/cuda:12.1.1-cudnn8-runtime-ubuntu22.04
 
-# Prevent interactive dialogs from halting the build
-ENV DEBIAN_FRONTEND=noninteractive
+RUN apt-get update && apt-get install -y python3.10 python3-pip
+RUN python3.10 -m pip install uv
 
-RUN apt update -y && apt install -y \
-  build-essential \
-  libcairo2-dev \
-  libhdf5-dev \
-  pkg-config \
-  python3-dev \
-  python3-openssl && \
-  apt clean && \
-  rm -rf /var/lib/apt/lists/*
-
-COPY ./pip/cuda_requirements.txt ./pip/cuda_requirements.txt
-RUN pip install -U pip && \
-  pip install --no-cache-dir \
-  --trusted-host pypi.python.org \
-  --trusted-host files.pythonhosted.org \
-  --trusted-host pypi.org \
-  --trusted-host data.dgl.ai \
-  --trusted-host download.pytorch.org \
-  -r ./pip/cuda_requirements.txt
-
-COPY ./pip/dev_requirements.txt ./pip/dev_requirements.txt
-RUN pip install -U pip && \
-  pip install --no-cache-dir \
-  --trusted-host pypi.python.org \
-  --trusted-host files.pythonhosted.org \
-  --trusted-host pypi.org \
-  --trusted-host data.dgl.ai \
-  --trusted-host download.pytorch.org \
-  -r ./pip/dev_requirements.txt
-
-COPY setup.py .
+WORKDIR /app
+COPY pyproject.toml .
+COPY atomsci ./atomsci
 COPY VERSION .
-COPY build.sh .
-COPY ./atomsci atomsci/
 
-# Install atomsci-ampl
-RUN ./build.sh
-RUN pip install -e .
+RUN uv sync --extra cuda
 
-# Clean up
-RUN apt purge -y build-essential python3-dev && \
-  apt autoremove -y && \
-  rm -rf /var/lib/apt/lists/*
-RUN rm -rf /root/.cache/pip
+CMD ["uv", "run", "python3.10", "-c", "import atomsci; print('cuda ok')"]
diff --git a/Dockerfile.rocm b/Dockerfile.rocm
new file mode 100644
index 00000000..c75e3f71
--- /dev/null
+++ b/Dockerfile.rocm
@@ -0,0 +1,22 @@
+FROM rocm/dev-ubuntu-22.04:6.1
+
+RUN apt-get update && apt-get install -y \
+    python3.10 \
+    python3-pip \
+    python3.10-venv \
+    build-essential \
+    git \
+    curl \
+    ca-certificates
+
+RUN python3.10 -m pip install --no-cache-dir uv
+
+WORKDIR /app
+
+COPY pyproject.toml .
+COPY VERSION .
+COPY atomsci ./atomsci
+
+RUN uv sync --extra rocm --group docker --no-dev
+
+CMD ["uv", "run", "python3.10", "-c", "import atomsci; print('rocm ok')"]
diff --git a/Makefile b/Makefile
index 78c7d44e..71be804a 100644
--- a/Makefile
+++ b/Makefile
@@ -46,8 +46,9 @@ VENV ?= atomsci-env
 # Work Directory
 WORK_DIR ?= work
 
-.PHONY: build-docker install install-dev install-system install-venv jupyter-notebook jupyter-lab \
-	pytest ruff ruff-fix setup uninstall uninstall-dev uninstall-system uninstall-venv
+.PHONY: update-lock-cpu update-lock-cuda update-lock-rocm update-lock-mchip sync-cpu sync-cuda sync-rocm sync-mchip  \
+        build-docker install install-dev install-system install-venv jupyter-notebook jupyter-lab \
+	pytest ruff ruff-fix setup
 
 # Load Docker image
 load-docker:
@@ -67,7 +68,7 @@ save-docker:
 
 # Build Docker image
 build-docker:
-	@echo "Building Docker image for $(PLATFORM)"
+	@echo "Building Docker image for $(PLATFORM) <cpu|gpu|rocm|arm>"
 	docker buildx build -t $(IMAGE_REPO):$(TAG) --build-arg ENV=$(ENV) $(PLATFORM_ARG) --load -f Dockerfile.$(PLATFORM) .
 
 install: install-system
@@ -123,7 +124,7 @@ pytest-integrative:
 pytest-unit:
 	@echo "Running unit tests"
 	docker run -v $(shell pwd)/$(WORK_DIR):/$(WORK_DIR) $(IMAGE_REPO):$(TAG) \
-		       /bin/bash -l -c "cd atomsci/ddm/test/unit && python3.9 -m pytest --capture=sys --capture=fd --cov=atomsci -vv"
+		       /bin/bash -l -c "cd atomsci/ddm/test/unit && python3.10 -m pytest --capture=sys --capture=fd --cov=atomsci -vv"
 
 # Run ruff linter
 ruff:
@@ -144,7 +145,7 @@ setup:
 	@echo "Removing old environment"
 	rm -rf $(VENV)/ || true
 	@echo "Creating new venv"
-	python3.9 -m venv $(VENV)/
+	python3.10 -m venv $(VENV)/
 	$(VENV)/bin/pip install -U pip
 	@echo "Installing dependencies"
 	@if [ "$(PLATFORM)" = "gpu" ]; then \
@@ -155,19 +156,30 @@ setup:
 	$(VENV)/bin/pip install -r pip/dev_requirements.txt
 	$(MAKE) install-venv
 
-uninstall: uninstall-system
+# for uv env
 
-# Uninstall atomsci-ampl from user space
-uninstall-dev:
-	@echo "Uninstalling atomsci-ampl for user"
-	$(PYTHON_BIN) -m pip uninstall atomsci-ampl --user --yes
+# use these to create/update a lock env. for maintainers only
+update-lock-cpu:
+	./update_uv_lock.sh cpu
 
-# Uninstall atomsci-ampl system-wide
-uninstall-system:
-	@echo "Uninstalling atomsci-ampl from $(PYTHON_BIN)"
-	$(PYTHON_BIN) -m pip uninstall atomsci-ampl --yes
+update-lock-cuda:
+	./update_uv_lock.sh cuda
 
-# Uninstall atomsci-ampl from virtual environment
-uninstall-venv:
-	@echo "Uninstalling atomsci-ampl from $(VENV)/"
-	$(VENV)/bin/python -m pip uninstall atomsci-ampl --yes
+update-lock-rocm:
+	./update_uv_lock.sh rocm
+
+update-lock-mchip:
+	./update_uv_lock.sh mchip
+
+# use the matching lockfile to create or refresh .venv-<platform>. for general users
+sync-cpu:
+	./sync_uv_env.sh cpu
+
+sync-cuda:
+	./sync_uv_env.sh cuda
+
+sync-rocm:
+	./sync_uv_env.sh rocm
+
+sync-mchip:
+	./sync_uv_env.sh mchip
\ No newline at end of file
diff --git a/Makefile.md b/Makefile.md
index 73965288..6050d4cf 100644
--- a/Makefile.md
+++ b/Makefile.md
@@ -39,13 +39,6 @@ This Makefile is designed to manage Jupyter environments, Docker images, and the
   - `make save-docker`: Save the Docker image to a tarball (`ampl-$(PLATFORM)-$(ENV).tar.gz`).
   - `make build-docker`: Build the Docker image for the specified platform and environment.
 
-- **Installation:**
-
-  - `make install`: Install `atomsci-ampl` system-wide.
-  - `make install-dev`: Install `atomsci-ampl` in user space.
-  - `make install-system`: Install `atomsci-ampl` system-wide.
-  - `make install-venv`: Install `atomsci-ampl` in a virtual environment.
-
 - **Jupyter Notebook and Lab:**
 
   - `make jupyter-notebook`: Start a Jupyter Notebook server.
@@ -63,13 +56,6 @@ This Makefile is designed to manage Jupyter environments, Docker images, and the
 
   - `make shell`: Go inside the container's shell.
 
-- **Setup and Uninstallation:**
-  - `make setup`: Set up a virtual environment and install dependencies.
-  - `make uninstall`: Uninstall `atomsci-ampl` system-wide.
-  - `make uninstall-dev`: Uninstall `atomsci-ampl` from user space.
-  - `make uninstall-system`: Uninstall `atomsci-ampl` system-wide.
-  - `make uninstall-venv`: Uninstall `atomsci-ampl` from the virtual environment.
-
 ### **Usage Examples**
 
 #### **Managing Docker Images**
@@ -108,10 +94,10 @@ make jupyter-lab
 
 #### **Setting Up the Development Environment**
 
-To set up a virtual environment with the appropriate dependencies:
+To set up a virtual environment with a specified platform:
 
 ```bash
-make setup
+make sync-<cpu|cuda|rocm|mchip>
 ```
 
 This Makefile provides a comprehensive approach to managing Docker images, running Jupyter servers, and handling the installation and setup of your development environment. It streamlines workflows, making it easier to maintain and develop the `atomsci-ampl` project.
diff --git a/README.md b/README.md
index 97beb1ca..859e9770 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,4 @@
-[![Develop test](https://github.com/ATOMScience-org/AMPL/actions/workflows/pytest.yml/badge.svg)](https://github.com/ATOMScience-org/AMPL/actions/workflows/pytest.yml)  [![Linter](https://github.com/ATOMScience-org/AMPL/actions/workflows/lint.yml/badge.svg)](https://github.com/ATOMScience-org/AMPL/actions/workflows/lint.yml)    [![Documentation Status](https://readthedocs.org/projects/ampl/badge/?version=stable)](https://ampl.readthedocs.io/en/latest/?badge=stable) 
+[![Develop test](https://github.com/ATOMScience-org/AMPL/actions/workflows/pytest.yml/badge.svg)](https://github.com/ATOMScience-org/AMPL/actions/workflows/pytest.yml)  [![Linter](https://github.com/ATOMScience-org/AMPL/actions/workflows/lint.yml/badge.svg)](https://github.com/ATOMScience-org/AMPL/actions/workflows/lint.yml)    [![Documentation Status](https://readthedocs.org/projects/ampl/badge/?version=stable)](https://ampl.readthedocs.io/en/latest/?badge=stable)
  [![codecov](https://codecov.io/gh/ATOMScience-org/AMPL/graph/badge.svg)](https://codecov.io/gh/ATOMScience-org/AMPL)
 
 ![GitHub Release](https://img.shields.io/github/v/release/ATOMScience-org/AMPL)  [![License](http://img.shields.io/:license-mit-blue.svg)](https://github.com/ATOMScience-org/AMPL/blob/master/LICENSE)   [![LinkedIn](https://custom-icon-badges.demolab.com/badge/LinkedIn-0A66C2?logo=linkedin-white&logoColor=fff)](https://www.linkedin.com/company/atomscience) [![YouTube](https://img.shields.io/badge/YouTube-%23FF0000.svg?logo=YouTube&logoColor=white)](https://www.youtube.com/channel/UCOF6zZ7ltGwopYCoOGIFM-w)
@@ -10,26 +10,39 @@ An open-source, end-to-end software pipeline for data curation, model building,
 
 *Created by the [Accelerating Therapeutics for Opportunities in Medicine (ATOM) Consortium](https://atomscience.org)*
 
-<img src="atomsci/ddm/docs/ATOM_cymatics_black_wordmark.jpg" width="370" height="100" class="center"></img>
-
+![AMPL logo](https://raw.githubusercontent.com/ATOMScience-org/AMPL/master/assets/ATOM_cymatics_black_wordmark.jpg)
 
 The ATOM Modeling PipeLine (AMPL) extends the functionality of DeepChem and supports an array of machine learning and molecular featurization tools to predict key potency, safety and pharmacokinetic-relevant parameters. AMPL has been benchmarked on a large collection of pharmaceutical datasets covering a wide range of parameters. This is a living software project with active development. Check back for continued updates. Feedback is welcomed and appreciated, and the project is open to contributions! An [article describing the AMPL project](https://pubs.acs.org/doi/abs/10.1021/acs.jcim.9b01053) was published in JCIM. The AMPL pipeline documentation is available [here](https://ampl.readthedocs.io/en/latest/pipeline.html).
 
 Check out our new tutorial series that walks through AMPL's end-to-end modeling pipeline to build a machine learning model! View them in our [docs](https://ampl.readthedocs.io/en/latest/) or as Jupyter notebooks in our [repo](https://github.com/ATOMScience-org/AMPL/tree/master/atomsci/ddm/examples/tutorials).
 
-![Static Badge](https://img.shields.io/badge/Announcement-1.7.0-blue)
+![Static Badge](https://img.shields.io/badge/Announcement-1.8.0-blue)
 
-In addition to our written tutorials, we now provide a series of video tutorials on our YouTube channel, [ATOMScience-org](https://www.youtube.com/channel/UCOF6zZ7ltGwopYCoOGIFM-w). These videos are created to assist users in exploring and leveraging AMPL's robust capabilities. We provided a playlist for easy streamlined Learning: 
+In addition to our written tutorials, we now provide a series of video tutorials on our YouTube channel, [ATOMScience-org](https://www.youtube.com/channel/UCOF6zZ7ltGwopYCoOGIFM-w). These videos are created to assist users in exploring and leveraging AMPL's robust capabilities. We provided a playlist for easy streamlined Learning:
 
 [![AMPL Tutorial Playlist](https://img.shields.io/badge/AMPL_Tutorial_Playlist-%23FF0000.svg?logo=YouTube&logoColor=white)](https://www.youtube.com/playlist?list=PLe85Q-Gf8eFgYGQmUDSTlSjJorQZyDG8E)
 
 ---
 ## Table of contents
-- [Install](#install)
-   - [Quick Install](#installation-quick-summary)
-   - [Jupyter kernel](#create-jupyter-notebook-kernel-optional)
-   - [Docker](#install-with-docker)
-   - [Uninstall](#uninstall)
+- [Installation](#installation)
+  - [Install the published package from PyPI](#install-the-published-package-from-pypi)
+    - [Get the latest release version from PyPI](#get-the-latest-release-version-from-pypi)
+  - [Set up an `uv` environment for development](#set-up-an-uv-environment-for-development)
+    - [What is `uv`?](#what-is-uv)
+    - [Requirements](#requirements)
+    - [Install `uv`](#install-uv)
+    - [Choose your platform](#choose-your-platform)
+    - [Create or refresh your environment](#create-or-refresh-your-environment)
+    - [Daily use](#daily-use)
+    - [Platform lockfiles](#platform-lockfiles)
+    - [For maintainers only](#for-maintainers-only)
+    - [Troubleshooting](#troubleshooting)
+      - [`uv` not found](#uv-not-found)
+      - [Wrong Python or missing `pytest`](#wrong-python-or-missing-pytest)
+      - [Import or package problems](#import-or-package-problems)
+  - [Building AMPL for Local Development](#building-ampl-for-local-development)
+    - [Build and install from a local clone](#build-and-install-from-a-local-clone)
+    - [Optional, for LLNL LC only](#optional-for-llnl-lc-only)
 - [AMPL Features](#ampl-features)
 - [Running AMPL](#running-ampl)
 - [Tests](#tests)
@@ -37,132 +50,234 @@ In addition to our written tutorials, we now provide a series of video tutorials
 - [Advanced testing](#advanced-testing)
 - [Tutorials](#ampl-tutorials)
 - [Development](#development)
-- [Project information](#project-information)  
+- [Project information](#project-information)
 - [Suggestions or Report Issues](#support-suggestions-or-report-issues)
 
 ## Useful links
 - [Pipeline parameters (options)](atomsci/ddm/docs/PARAMETERS.md)
 - [Library documentation](https://ampl.readthedocs.io/en/latest/index.html)
+
 ---
-## Install
-AMPL 1.6 supports Python 3.9 CPU or CUDA-enabled machines using CUDA 11.8 on Linux. All other systems are experimental. For a quick install summary, see [here](#install-summary). We do not support other CUDA versions because there are multiple ML package dependency conflicts that can occur. For more information you can look at [DeepChem](https://deepchem.readthedocs.io/en/latest/get_started/installation.html), [TensorFlow](https://www.tensorflow.org/install/pip), [PyTorch](https://pytorch.org/get-started/locally/), [DGL](https://www.dgl.ai/pages/start.html).
 
-For installation on Apple Silicon M Chips, please see the Docker container instructions.
+## Installation
+
+AMPL supports Python 3.10 and provides platform-specific environments for `cpu`, `cuda`, `rocm`, and `mchip`.
+
+For dependency-specific installation details, see:
+- [DeepChem](https://deepchem.readthedocs.io/en/latest/get_started/installation.html)
+- [TensorFlow](https://www.tensorflow.org/install/pip)
+- [PyTorch](https://pytorch.org/get-started/locally/)
+- [DGL](https://www.dgl.ai/pages/start.html)
+
+AMPL uses [`uv`](https://docs.astral.sh/uv/) for Python environment and dependency management.
+
+### Install the published package from PyPI
 
-### Create pip environment
+Use this option if you want to install the released `atomsci-ampl` package without cloning this repository.
 
-#### 1. Create a virtual env with Python 3.9 
-Make sure to create your virtual env in a convenient directory that has at least 12Gb space.
+If you are working from a local checkout of this repo, use the local `uv` environment workflow above instead.
 
-Go to the directory where the new environment directory be installed in. Define an environment variable - "ENVROOT".
+> **Note:** AMPL requires Python 3.10. For LLNL users on LC, run `module load python/3.10.8`.
 
+### Get the latest release version from PyPI
+
+Create or activate a Python 3.10 environment, then install:
 ```bash
-export ENVROOT=~/workspace # for LLNL LC users, use your workspace
-or
-export ENVROOT=~ # or the directory as your environment root
+pip install atomsci-ampl
+```
+
+Or use `uv` install:
+
+Example:
+```bash
+uv venv --python 3.10 <.venv_name>
+source <.venv_name>/bin/activate
+uv pip install atomsci-ampl
 ```
 
-> *We use "workspace" and "atomsci-env" as an example here.*
+---
+
+### Set up an `uv` environment for local development
+
+Use this workflow if you want to run or develop AMPL from a local clone of this repository.
+
+#### What is `uv`?
+
+`uv` is the Python environment and dependency tool used by this project. It creates virtual environments and installs the packages defined in `pyproject.toml`.
 
+#### Requirements
+
+| Item | Value |
+|---|---|
+| Python | 3.10 |
+| Supported range | `>=3.10,<3.11` |
+| Platforms | `cpu`, `cuda`, `rocm`, `mchip` |
+
+#### Install `uv`
+
+Install `uv` with one of the following:
+```bash
+curl -LsSf https://astral.sh/uv/install.sh | sh
+```
+or
+```bash
+pip install uv
+```
+Verify the installation:
 ```bash
-# LLNL only: 
-# module load python/3.9.12
-cd $ENVROOT
-python3.9 -m venv atomsci-env
+uv --version
 ```
+#### Choose your platform
 
-#### 2. Activate the environment
+| Platform | Use this if... | Environment |
+|---|---|---|
+| `cpu` | you want a CPU-only environment | `.venv-cpu` |
+| `cuda` | you are using NVIDIA GPUs on Linux | `.venv-cuda` |
+| `rocm` | you are using AMD GPUs | `.venv-rocm` |
+| `mchip` | you are on Apple Silicon | `.venv-mchip` |
+
+#### Create or refresh your environment
+
+For most users, use the `make sync-<platform>` commands:
 ```bash
-source $ENVROOT/atomsci-env/bin/activate
+make sync-cpu
 ```
+Other options:
+```bash
+make sync-cuda
+make sync-rocm
+make sync-mchip
+```
+Then activate the environment:
+```bash
+source .venv-cpu/bin/activate
+```
+Replace `cpu` with your platform as needed.
 
-#### 3. Update pip
+Example:
 ```bash
-pip install pip --upgrade
+make sync-cpu
+source .venv-cpu/bin/activate
 ```
+#### Daily use
 
-#### 4. Clone AMPL repository
+If the environment is already working, you can usually just activate it:
+```bash
+source .venv-cpu/bin/activate
+```
+If commands like `pytest` are missing, or imports fail, refresh the environment:
 ```bash
-git clone https://github.com/ATOMScience-org/AMPL.git 
+make sync-cpu
+source .venv-cpu/bin/activate
 ```
+#### Platform lockfiles
 
-#### 5. Install pip requirements
-Depending on system performance, creating the environment can take some time.
-> ***Note:*** *Based on which environment (CPU or CUDA) to run on, only run one of the following:*
+This project keeps one lockfile per platform:
+
+| Platform | Lockfile | Environment |
+|---|---|---|
+| `cpu` | `uv.lock.cpu` | `.venv-cpu` |
+| `cuda` | `uv.lock.cuda` | `.venv-cuda` |
+| `rocm` | `uv.lock.rocm` | `.venv-rocm` |
+| `mchip` | `uv.lock.mchip` | `.venv-mchip` |
+
+The `make sync-<platform>` commands use the matching lockfile to create or refresh `.venv-<platform>`.
+
+#### For maintainers only
+
+If dependencies change in `pyproject.toml`, you **must** regenerate the platform lockfile:
 
-- CPU-only installation:
 ```bash
-cd AMPL/pip
-pip install -r cpu_requirements.txt
+make update-lock-cpu
 ```
 
-- CUDA installation:
+Other supported targets:
+```bash
+make update-lock-cuda
+make update-lock-rocm
+make update-lock-mchip
+```
 
-First load the CUDA module. Then run cuda specific package install.
+These commands create the lockfile if it does not already exist, or update it if it does.
 
+**Any changes to `pyproject.toml` or `uv.lock.<platform>` must be committed and pushed to the AMPL GitHub repository. Do not leave regenerated lockfiles uncommitted.**
+
+Most users do not need to run `make update-lock-<platform>`.
+
+#### Troubleshooting
+
+##### `uv` not found
 ```bash
-cd AMPL/pip
-# LLNL only: 
-# module load cuda/11.8
-pip install -r cuda_requirements.txt
+uv --version
 ```
-If you get `out of memory` errors, try setting these environment variables:
+If this fails, install `uv` and start a new shell.
+
+##### Wrong Python or missing `pytest`
+
+Check:
+```bash
+which python
+python --version
+which pytest
+```
+They should point into the active environment, for example:
+```bash
+.venv-cpu/bin/python
+.venv-cpu/bin/pytest
 ```
-export LD_LIBRARY_PATH=<your_env>/lib:$LD_LIBRARY_PATH
-export PYTHONUSERBASE=<your_env>
-export OPENBLAS_NUM_THREADS=1
-export OMP_NUM_THREADS=48
-export PYTORCH_HIP_ALLOC_CONF=gargage_collection_threshold:0.9,max_split_size_mb:128
-export TF_FORCE_GPU_ALLOW_GROWTH=true
+If not, refresh the environment:
+```bash
+make sync-cpu
+source .venv-cpu/bin/activate
 ```
+##### Import or package problems
 
-- Install pytest, plotting packages for development, test use.
+Refresh the environment:
+```bash
+make sync-cpu
+source .venv-cpu/bin/activate
+```
+### Building AMPL for Local Development
 
+If you want to develop AMPL locally from a repository checkout:
 ```bash
-cd AMPL/pip
-pip install -r dev_requirements.txt
+git clone https://github.com/ATOMScience-org/AMPL.git
+cd AMPL
+./install-dev.sh
 ```
-#### 6. *(Optional) LLNL LC only*: if you use [model_tracker](https://ampl.readthedocs.io/en/latest/pipeline.html#module-pipeline.model_tracker), install atomsci.clients
+This installs the package from the local source tree, so code changes are available without reinstalling.
+
+#### Build and install from a local clone
+
+If you want to build the package locally and then install the built artifact:
+```bash
+git clone https://github.com/ATOMScience-org/AMPL.git
+cd AMPL
+./build.sh
+./install.sh
+```
+#### Optional, for LLNL LC only
+
+If you use [model_tracker](https://ampl.readthedocs.io/en/latest/pipeline.html#module-pipeline.model_tracker), install `atomsci.clients`:
 ```bash
-# LLNL only: required for ATOM model_tracker
 pip install -r clients_requirements.txt
 ```
 
-### Install AMPL
-Run the following to build the "atomsci" modules. This is required.
+---
+
+## Create jupyter notebook kernel (optional)
 
+To run AMPL from Jupyter Notebook, first activate your environment and then run:
 ```bash
-# return to AMPL parent directory
-cd ..
-./build.sh
-pip install -e .
+python -m ipykernel install --user --name atomsci-env
 ```
 ---
-## Installation Quick Summary
+#### *(Optional) LLNL LC only*: if you use [model_tracker](https://ampl.readthedocs.io/en/latest/pipeline.html#module-pipeline.model_tracker), install atomsci.clients
 ```bash
-export ENVROOT=~/workspace           # set ENVROOT example
-# LLNL only:
-# module load python/3.9.12
-
-python3.9 -m venv atomsci-env        # create environment with Python 3.9
-source $ENVROOT/atomsci-env/bin/activate 
-pip install pip --upgrade               
-
-git clone https://github.com/ATOMScience-org/AMPL.git # clone AMPL
-cd AMPL/pip    
-# LLNL only:
-# If use CUDA: 
-# module load cuda/11.8                         
-pip install -r cpu_requirements.txt    # install cpu_requirements.txt OR cuda_requirements.txt  
-pip install -r dev_requirements.txt    # install pytest, plotting packages.
-
 # LLNL only: required for ATOM model_tracker
-# pip install -r clients_requirements.txt
-
-cd ..                                   
-./build.sh                            
-pip install -e .                        
+pip install -r clients_requirements.txt
 ```
+
 ---
 ## Create jupyter notebook kernel (optional)
 To run AMPL from Jupyter Notebook. To setup a new kernel, first activate your environment and then run the following command:
@@ -175,7 +290,7 @@ python -m ipykernel install --user --name atomsci-env
 ## Install with Docker
 - Download and install Docker Desktop.
   - https://www.docker.com/get-started
-- Create a workspace folder to mount with Docker environment and transfer files. 
+- Create a workspace folder to mount with Docker environment and transfer files.
 - Get the Docker image and run it. Since 1.6.3, there are some changes with the AMPL Docker.
 
 To retrieve, run version 1.6.2 or earlier, please specify the desired version tag:
@@ -184,7 +299,7 @@ To retrieve, run version 1.6.2 or earlier, please specify the desired version ta
   docker run -it -p 8888:8888 -v </local_workspace_folder>:</directory_in_docker> atomsci/atomsci-ampl:v1.6.2
   ```
 
-For AMPL versions 1.6.3 and later, we offer downloadable images for various platforms (CPU, GPU or Linux/ARM64). To run a Docker container, be sure to append `bash` at the end of the command to open a bash session. 
+For AMPL versions 1.6.3 and later, we offer downloadable images for various platforms (CPU, GPU or Linux/ARM64). To run a Docker container, be sure to append `bash` at the end of the command to open a bash session.
 
   ```
   docker pull atomsci/atomsci-ampl:latest-<platform> # can be cpu, gpu, or arm (for arm64 chip)
@@ -204,22 +319,11 @@ For additional options related to building, running, and other Docker developmen
 
 ---
 
-## Uninstall
-To remove AMPL from a pip environment use:
-```bash
-pip uninstall atomsci-ampl
-```
-
 To remove an entire virtual environment named "atomsci-env":
 ```bash
 rm -rf $ENVROOT/atomsci-env
 ```
 
-To remove cached packages and clear space:
-```bash
-pip cache purge
-```
-
 ---
 ## AMPL Features
 <details><summary>AMPL enables tasks for modeling and prediction from data ingestion to data analysis and can be broken down into the following stages:</summary>
@@ -233,28 +337,27 @@ pip cache purge
 - Extended connectivity fingerprints (ECFP)
 - Graph convolution latent vectors from DeepChem
 - Chemical descriptors from Mordred package
-- Descriptors generated by MOE (requires MOE license)  
+- Descriptors generated by MOE (requires MOE license)
 
 ### 3. Model training and tuning
 - Test set selection
 - Cross-validation
 - Uncertainty quantification
-- Hyperparameter optimization  
 
 ### 4. Supported models
 - scikit-learn random forest models
 - XGBoost models
 - Fully connected neural networks
-- Graph convolution models  
+- Graph convolution models
 
 ### 5. Visualization and analysis
-- Visualization and analysis tools  
+- Visualization and analysis tools
 </details>
-Details of running specific features are within the [parameter (options) documentation](#Pipeline-parameters). More detailed documentation is in the [library documentation](#Library-documentation).  
+Details of running specific features are within the [parameter (options) documentation](atomsci/ddm/docs/PARAMETERS.md). More detailed documentation is in the [library documentation](https://ampl.readthedocs.io/en/latest/).
 
 ---
 ## Running AMPL
-AMPL can be run from the command line or by importing into Python scripts and Jupyter notebooks.  
+AMPL can be run from the command line or by importing into Python scripts and Jupyter notebooks.
 
 ### Python scripts and Jupyter notebooks
 AMPL can be used to fit and predict molecular activities and properties by importing the appropriate modules. See the [examples](atomsci/ddm/examples/) for more descriptions on how to fit and make predictions using AMPL.
@@ -263,13 +366,13 @@ AMPL can be used to fit and predict molecular activities and properties by impor
 AMPL includes many parameters to run various model fitting and prediction tasks.
 - Pipeline options (parameters) can be set within JSON files containing a parameter list.
 - The parameter list with detailed explanations of each option can be found at [atomsci/ddm/docs/PARAMETERS.md](atomsci/ddm/docs/PARAMETERS.md).
-- Example pipeline JSON files can be found in the tests directory and the example directory.  
+- Example pipeline JSON files can be found in the tests directory and the example directory.
 
 ### Library documentation
 AMPL includes detailed docstrings and comments to explain the modules. Full HTML documentation of the Python library is available with the package at [https://ampl.readthedocs.io/en/latest/](https://ampl.readthedocs.io/en/latest/).
 
 ### More information on AMPL usage
-- More information on AMPL usage can be found in [Advanced AMPL usage](#advanced-ampl-usage)   
+- More information on AMPL usage can be found in [Advanced AMPL usage](#advanced-ampl-usage)
 
 ---
 ## Tests
@@ -281,7 +384,7 @@ source $ENVROOT/atomsci-env/bin/activate # activate your pip environment.
 cd atomsci/ddm/test/integrative/delaney_RF
 pytest
 ```
-> ***Note***: *This test generally takes a few minutes on a modern system*  
+> ***Note***: *This test generally takes a few minutes on a modern system*
 
 The important files for this test are listed below:
 
@@ -299,7 +402,7 @@ The important files for this test are listed below:
 AMPL can **fit** models from the command line with:
 ```bash
 python model_pipeline.py --config_file filename.json # [filename].json is the name of the config file
-```  
+```
 
 To get more info on an AMPL config file, please refer to:
 
@@ -308,45 +411,46 @@ To get more info on an AMPL config file, please refer to:
   - [AMPL Tutorials](atomsci/ddm/examples/tutorials)
 
 ### Hyperparameter optimization
-<details><summary>Hyperparameter optimization for AMPL model fitting is available to run on SLURM clusters or with [HyperOpt](https://hyperopt.github.io/hyperopt/) (Bayesian Optimization). To run Bayesian Optimization, the following steps can be followed.</summary>
+<details><summary>Hyperparameter optimization for AMPL model fitting is available to run on SLURM clusters or with [Optuna](https://optuna.readthedocs.io/) (Bayesian Optimization). To run Bayesian Optimization, the following steps can be followed.</summary>
 
-1. (Optional) Install HyperOpt with "pip install hyperopt"
+1. (Optional) Install Optuna with "pip install optuna"
 2. Pre-split your dataset with computed_descriptors if you want to use Mordred/MOE/RDKit descriptors.
 3. In the config JSON file, set the following parameters.
-   
+
    - "hyperparam": "True"
-   - "search_type": "hyperopt"
+   - "search_type": "optuna"
    - "descriptor_type": "mordred_filtered,rdkit_raw" (use comma to separate multiple values)
    - "model_type": "RF|20" (the number after | is the number of evaluations of Bayesian Optimization)
    - "featurizer": "ecfp,computed_descriptors" (use comma if you want to try multiple featurizers, note the RF and graphconv are not compatible)
    - "result_dir": "/path/to/save/the/final/results,/temp/path/to/save/models/during/optimization" (Two paths separated by a comma)
-  
+
    RF model specific parameters:
    - "rfe": "uniformint|8,512", (RF number of estimators)
    - "rfd": "uniformint|8,512", (RF max depth of the decision tree)
    - "rff": "uniformint|8,200", (RF max number of features)
-  
+
     Use the following schemes to define the searching domains
-    
+
     method|parameter1,parameter2...
-    
-    method: supported searching schemes in HyperOpt include: choice, uniform, loguniform, uniformint, see https://github.com/hyperopt/hyperopt/wiki/FMin for details.
-    
+
+    method: supported searching schemes in Optuna include: choice, uniform, loguniform, uniformint. For details, see the [Optuna documentation](https://optuna.readthedocs.io/en/stable/reference/generated/optuna.trial.Trial.html).
+
     parameters:
       - choice: all values to search from, separated by comma, e.g. choice|0.0001,0.0005,0.0002,0.001
-      - uniform: low and high bound of the interval to serach, e.g. uniform|0.00001,0.001
-      - loguniform: low and high bound (in natural log) of the interval to serach, e.g. uniform|-13.8,-6.9
-      - uniformint: low and high bound of the interval as integers, e.g. uniforming|8,256
-  
+      - uniform: low and high bound of the interval to search, e.g. uniform|0.00001,0.001
+      - loguniform: low and high bound (in natural log) of the interval to search, e.g. loguniform|-13.8,-6.9
+        - **Note**: For backwards compatibility, loguniform values are submitted to Optuna in log scale. Although Optuna supports non-log-scaled value ranges for log-uniform distributions, we maintain the original log-scaled specification format to ensure consistency with existing configurations.
+      - uniformint: low and high bound of the interval as integers, e.g. uniformint|8,256
+
     NN model specific parameters:
      - "lr": "loguniform|-13.8,-6.9", (learning rate)
      - "ls": "uniformint|3|8,512", (layer_sizes)
         - The number between two bars (|) is the number of layers, namely 3 layers, each one with 8~512 nodes
-        - Note that the number of layers (number between two |) can not be changed during optimization, if you want to try different number of layers, just run several optimizations. 
+        - Note that the number of layers (number between two |) can not be changed during optimization, if you want to try different number of layers, just run several optimizations.
      - "dp": "uniform|3|0,0.4", (dropouts)
         - 3 layers, each one has a dropout range from 0 to 0.4
-        - Note that the number of layers (number between two |) can not be changed during optimization, if you want to try different number of layers, just run several optimizations. 
-    
+        - Note that the number of layers (number between two |) can not be changed during optimization, if you want to try different number of layers, just run several optimizations.
+
     XGBoost model specific parameters:
      - "xgbg": "uniform|0,0.4", (xgb_gamma, Minimum loss reduction required to make a further partition on a leaf node of the tree)
      - "xgbl": "loguniform|-6.9,-2.3", (xgb_learning_rate, Boosting learning rate (xgboost's "eta"))
@@ -356,14 +460,14 @@ To get more info on an AMPL config file, please refer to:
     ```
     python hyperparam_search_wrapper.py --config_file filename.json
     ```
-    
+
 5. Save a checkpoint to continue it later.
-    
+
     To save a checkpoint file of the hyperparameter search job, you want to set the following two parameters.
     - "hp_checkpoint_save": "/path/to/the/checkpoint/file.pkl"
     - "hp_checkpoint_load": "/path/to/the/checkpoint/file.pkl"
-    
-    If the "hp_checkpoint_load" is provided, the hyperparameter search will continue from the checkpoint. 
+
+    If the "hp_checkpoint_load" is provided, the hyperparameter search will continue from the checkpoint.
 </details>
 
 ---
@@ -393,7 +497,7 @@ sbatch pytest_slurm.sh
 ### Running tests without internet access
 <details><summary>AMPL works without internet access. Curation, fitting, and prediction do not require internet access.</summary>
 
-However, the public datasets used in tests and examples are not included in the repo due to licensing concerns. These are automatically downloaded when the tests are run. 
+However, the public datasets used in tests and examples are not included in the repo due to licensing concerns. These are automatically downloaded when the tests are run.
 
 If a system does not have internet access, the datasets will need to be downloaded before running the tests and examples. From a system with internet access, run the following shell script to download the public datasets. Then, copy the AMPL directory to the offline system.
 
@@ -407,20 +511,20 @@ cd ../../..
 
 ---
 ## AMPL tutorials
-Please follow link, ["atomsci/ddm/examples/tutorials"](https://github.com/ATOMScience-org/AMPL/tree/master/atomsci/ddm/examples/tutorials), to access a collection of AMPL tutorial notebooks. The tutorial notebooks give an exhaustive coverage of AMPL features. The AMPL team has prepared the tutorials to help beginners understand the basics to advanced AMPL features, and a reference for advanced AMPL users. 
+Please follow link, ["atomsci/ddm/examples/tutorials"](https://github.com/ATOMScience-org/AMPL/tree/master/atomsci/ddm/examples/tutorials), to access a collection of AMPL tutorial notebooks. The tutorial notebooks give an exhaustive coverage of AMPL features. The AMPL team has prepared the tutorials to help beginners understand the basics to advanced AMPL features, and a reference for advanced AMPL users.
 
 ---
 ## Development
 ### Installing the AMPL for development
-Using "pip install -e ." will create a namespace package in your environment directory that points back to your git working directory, so every time you reimport a module you'll be in sync with your working code. Since site-packages is already in your sys.path, you won't have to fuss with PYTHONPATH or setting sys.path in your notebooks.  
+Using "pip install -e ." will create a namespace package in your environment directory that points back to your git working directory, so every time you reimport a module you'll be in sync with your working code. Since site-packages is already in your sys.path, you won't have to fuss with PYTHONPATH or setting sys.path in your notebooks.
 
 ### Code Push Policy
 It's recommended to use a development branch to do the work. After each release, there will be a branch opened for development.
 
-The policy is 
+The policy is
 
 1. Create a branch based off a development ("1.6.0 "for example) or "master" branch
-2. Create a pull request. Assign a reviewer to approve the code changes 
+2. Create a pull request. Assign a reviewer to approve the code changes
 
 > ***Note***:
 > Step 2 is required for pushing directly to "master". For a development branch, this step is recommended but not required.
@@ -429,7 +533,7 @@ The policy is
 The ["Google docstring"](https://github.com/google/styleguide/blob/gh-pages/pyguide.md#38-comments-and-docstrings) format is used in the AMPL code. When writing new code, please use the same Docstring style. Refer [here](https://www.sphinx-doc.org/en/master/usage/extensions/example_google.html#example-google) and [here](https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html) for examples.
 
 ### Versioning
-Versions are managed through GitHub tags on this repository.  
+Versions are managed through GitHub tags on this repository.
 
 ### Built with
 - [DeepChem](https://github.com/deepchem/deepchem): A rich repository of chemistry-specific model types and utilities
@@ -454,7 +558,7 @@ Versions are managed through GitHub tags on this repository.
 - Stacie Calad-Thomson <sub>(2)</sub>
 - Jim Brase <sub>(1)</sub>
 - Jonathan E. Allen <sub>(1)</sub>
-&nbsp;  
+&nbsp;
 
 ### Contributors
 - [Amanda Paulson](https://github.com/paulsonak) <sub>(5)</sub>
@@ -470,7 +574,7 @@ Versions are managed through GitHub tags on this repository.
 <sub>5. [University of California, San Francisco](https://www.ucsf.edu/)</sub>\
 <sub>6. [Schrodinger](https://www.schrodinger.com/)</sub>\
 <sub>7. [Leidos](https://www.leidos.com)</sub>
-&nbsp;  
+&nbsp;
 
 ### Support, Suggestions or Report Issues
 - If you have suggestions or like to report issues, please click [here](https://github.com/ATOMScience-org/AMPL/issues).
@@ -480,8 +584,8 @@ Versions are managed through GitHub tags on this repository.
 Thank you for contributing to AMPL!
 
 - Contributions must be submitted through pull requests.
-- All new contributions must adhere to the MIT license.  
-&nbsp;  
+- All new contributions must adhere to the MIT license.
+&nbsp;
 
 ### Release
 AMPL is distributed under the terms of the MIT license. All new contributions must be made under this license.
diff --git a/VERSION b/VERSION
index bd8bf882..27f9cd32 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-1.7.0
+1.8.0
diff --git a/assets/ATOM_cymatics_black_wordmark.jpg b/assets/ATOM_cymatics_black_wordmark.jpg
new file mode 100755
index 00000000..f6cea5e8
Binary files /dev/null and b/assets/ATOM_cymatics_black_wordmark.jpg differ
diff --git a/atomsci/__init__.py b/atomsci/__init__.py
deleted file mode 100644
index 80f4617e..00000000
--- a/atomsci/__init__.py
+++ /dev/null
@@ -1,8 +0,0 @@
-# coding: utf-8
-#
-# ddm __init__.py
-#
-
-import pkg_resources
-
-pkg_resources.declare_namespace(__name__)
diff --git a/atomsci/ddm/__init__.py b/atomsci/ddm/__init__.py
index c25fb4ee..1509b651 100644
--- a/atomsci/ddm/__init__.py
+++ b/atomsci/ddm/__init__.py
@@ -1,6 +1,21 @@
-import pkg_resources
+#import pkg_resources
+#try:
+#    __version__ = pkg_resources.require("atomsci-ampl")[0].version
+#except TypeError:
+#    pass
 try:
-    __version__ = pkg_resources.require("atomsci-ampl")[0].version
-except TypeError:
-    pass
+    from importlib.metadata import version, PackageNotFoundError
+except ImportError:
+    try:
+        from importlib_metadata import version, PackageNotFoundError
+    except ImportError:
+        version = None
+        PackageNotFoundError = Exception
 
+if version is not None:
+    try:
+        __version__ = version("atomsci-ampl")
+    except PackageNotFoundError:
+        __version__ = "unknown"
+else:
+    __version__ = "unknown"
diff --git a/atomsci/ddm/data/descriptor_sets_sources_by_descr_type.csv b/atomsci/ddm/data/descriptor_sets_sources_by_descr_type.csv
index 787842ae..825930b1 100644
--- a/atomsci/ddm/data/descriptor_sets_sources_by_descr_type.csv
+++ b/atomsci/ddm/data/descriptor_sets_sources_by_descr_type.csv
@@ -9,3 +9,5 @@ moe_informative,ASA+;ASA-;ASA_H;ASA_P;ASA;BCUT_PEOE_0;BCUT_PEOE_1;BCUT_PEOE_2;BC
 mordred_raw,ABC;ABCGG;nAcid;nBase;SpAbs_A;SpMax_A;SpDiam_A;SpAD_A;SpMAD_A;LogEE_A;VE1_A;VE2_A;VE3_A;VR1_A;VR2_A;VR3_A;nAromAtom;nAromBond;nAtom;nHeavyAtom;nSpiro;nBridgehead;nHetero;nH;nB;nC;nN;nO;nS;nP;nF;nCl;nBr;nI;nX;ATS0dv;ATS1dv;ATS2dv;ATS3dv;ATS4dv;ATS5dv;ATS6dv;ATS7dv;ATS8dv;ATS0d;ATS1d;ATS2d;ATS3d;ATS4d;ATS5d;ATS6d;ATS7d;ATS8d;ATS0s;ATS1s;ATS2s;ATS3s;ATS4s;ATS5s;ATS6s;ATS7s;ATS8s;ATS0Z;ATS1Z;ATS2Z;ATS3Z;ATS4Z;ATS5Z;ATS6Z;ATS7Z;ATS8Z;ATS0m;ATS1m;ATS2m;ATS3m;ATS4m;ATS5m;ATS6m;ATS7m;ATS8m;ATS0v;ATS1v;ATS2v;ATS3v;ATS4v;ATS5v;ATS6v;ATS7v;ATS8v;ATS0se;ATS1se;ATS2se;ATS3se;ATS4se;ATS5se;ATS6se;ATS7se;ATS8se;ATS0pe;ATS1pe;ATS2pe;ATS3pe;ATS4pe;ATS5pe;ATS6pe;ATS7pe;ATS8pe;ATS0are;ATS1are;ATS2are;ATS3are;ATS4are;ATS5are;ATS6are;ATS7are;ATS8are;ATS0p;ATS1p;ATS2p;ATS3p;ATS4p;ATS5p;ATS6p;ATS7p;ATS8p;ATS0i;ATS1i;ATS2i;ATS3i;ATS4i;ATS5i;ATS6i;ATS7i;ATS8i;AATS0dv;AATS1dv;AATS2dv;AATS3dv;AATS4dv;AATS5dv;AATS6dv;AATS7dv;AATS8dv;AATS0d;AATS1d;AATS2d;AATS3d;AATS4d;AATS5d;AATS6d;AATS7d;AATS8d;AATS0s;AATS1s;AATS2s;AATS3s;AATS4s;AATS5s;AATS6s;AATS7s;AATS8s;AATS0Z;AATS1Z;AATS2Z;AATS3Z;AATS4Z;AATS5Z;AATS6Z;AATS7Z;AATS8Z;AATS0m;AATS1m;AATS2m;AATS3m;AATS4m;AATS5m;AATS6m;AATS7m;AATS8m;AATS0v;AATS1v;AATS2v;AATS3v;AATS4v;AATS5v;AATS6v;AATS7v;AATS8v;AATS0se;AATS1se;AATS2se;AATS3se;AATS4se;AATS5se;AATS6se;AATS7se;AATS8se;AATS0pe;AATS1pe;AATS2pe;AATS3pe;AATS4pe;AATS5pe;AATS6pe;AATS7pe;AATS8pe;AATS0are;AATS1are;AATS2are;AATS3are;AATS4are;AATS5are;AATS6are;AATS7are;AATS8are;AATS0p;AATS1p;AATS2p;AATS3p;AATS4p;AATS5p;AATS6p;AATS7p;AATS8p;AATS0i;AATS1i;AATS2i;AATS3i;AATS4i;AATS5i;AATS6i;AATS7i;AATS8i;ATSC0c;ATSC1c;ATSC2c;ATSC3c;ATSC4c;ATSC5c;ATSC6c;ATSC7c;ATSC8c;ATSC0dv;ATSC1dv;ATSC2dv;ATSC3dv;ATSC4dv;ATSC5dv;ATSC6dv;ATSC7dv;ATSC8dv;ATSC0d;ATSC1d;ATSC2d;ATSC3d;ATSC4d;ATSC5d;ATSC6d;ATSC7d;ATSC8d;ATSC0s;ATSC1s;ATSC2s;ATSC3s;ATSC4s;ATSC5s;ATSC6s;ATSC7s;ATSC8s;ATSC0Z;ATSC1Z;ATSC2Z;ATSC3Z;ATSC4Z;ATSC5Z;ATSC6Z;ATSC7Z;ATSC8Z;ATSC0m;ATSC1m;ATSC2m;ATSC3m;ATSC4m;ATSC5m;ATSC6m;ATSC7m;ATSC8m;ATSC0v;ATSC1v;ATSC2v;ATSC3v;ATSC4v;ATSC5v;ATSC6v;ATSC7v;ATSC8v;ATSC0se;ATSC1se;ATSC2se;ATSC3se;ATSC4se;ATSC5se;ATSC6se;ATSC7se;ATSC8se;ATSC0pe;ATSC1pe;ATSC2pe;ATSC3pe;ATSC4pe;ATSC5pe;ATSC6pe;ATSC7pe;ATSC8pe;ATSC0are;ATSC1are;ATSC2are;ATSC3are;ATSC4are;ATSC5are;ATSC6are;ATSC7are;ATSC8are;ATSC0p;ATSC1p;ATSC2p;ATSC3p;ATSC4p;ATSC5p;ATSC6p;ATSC7p;ATSC8p;ATSC0i;ATSC1i;ATSC2i;ATSC3i;ATSC4i;ATSC5i;ATSC6i;ATSC7i;ATSC8i;AATSC0c;AATSC1c;AATSC2c;AATSC3c;AATSC4c;AATSC5c;AATSC6c;AATSC7c;AATSC8c;AATSC0dv;AATSC1dv;AATSC2dv;AATSC3dv;AATSC4dv;AATSC5dv;AATSC6dv;AATSC7dv;AATSC8dv;AATSC0d;AATSC1d;AATSC2d;AATSC3d;AATSC4d;AATSC5d;AATSC6d;AATSC7d;AATSC8d;AATSC0s;AATSC1s;AATSC2s;AATSC3s;AATSC4s;AATSC5s;AATSC6s;AATSC7s;AATSC8s;AATSC0Z;AATSC1Z;AATSC2Z;AATSC3Z;AATSC4Z;AATSC5Z;AATSC6Z;AATSC7Z;AATSC8Z;AATSC0m;AATSC1m;AATSC2m;AATSC3m;AATSC4m;AATSC5m;AATSC6m;AATSC7m;AATSC8m;AATSC0v;AATSC1v;AATSC2v;AATSC3v;AATSC4v;AATSC5v;AATSC6v;AATSC7v;AATSC8v;AATSC0se;AATSC1se;AATSC2se;AATSC3se;AATSC4se;AATSC5se;AATSC6se;AATSC7se;AATSC8se;AATSC0pe;AATSC1pe;AATSC2pe;AATSC3pe;AATSC4pe;AATSC5pe;AATSC6pe;AATSC7pe;AATSC8pe;AATSC0are;AATSC1are;AATSC2are;AATSC3are;AATSC4are;AATSC5are;AATSC6are;AATSC7are;AATSC8are;AATSC0p;AATSC1p;AATSC2p;AATSC3p;AATSC4p;AATSC5p;AATSC6p;AATSC7p;AATSC8p;AATSC0i;AATSC1i;AATSC2i;AATSC3i;AATSC4i;AATSC5i;AATSC6i;AATSC7i;AATSC8i;MATS1c;MATS2c;MATS3c;MATS4c;MATS5c;MATS6c;MATS7c;MATS8c;MATS1dv;MATS2dv;MATS3dv;MATS4dv;MATS5dv;MATS6dv;MATS7dv;MATS8dv;MATS1d;MATS2d;MATS3d;MATS4d;MATS5d;MATS6d;MATS7d;MATS8d;MATS1s;MATS2s;MATS3s;MATS4s;MATS5s;MATS6s;MATS7s;MATS8s;MATS1Z;MATS2Z;MATS3Z;MATS4Z;MATS5Z;MATS6Z;MATS7Z;MATS8Z;MATS1m;MATS2m;MATS3m;MATS4m;MATS5m;MATS6m;MATS7m;MATS8m;MATS1v;MATS2v;MATS3v;MATS4v;MATS5v;MATS6v;MATS7v;MATS8v;MATS1se;MATS2se;MATS3se;MATS4se;MATS5se;MATS6se;MATS7se;MATS8se;MATS1pe;MATS2pe;MATS3pe;MATS4pe;MATS5pe;MATS6pe;MATS7pe;MATS8pe;MATS1are;MATS2are;MATS3are;MATS4are;MATS5are;MATS6are;MATS7are;MATS8are;MATS1p;MATS2p;MATS3p;MATS4p;MATS5p;MATS6p;MATS7p;MATS8p;MATS1i;MATS2i;MATS3i;MATS4i;MATS5i;MATS6i;MATS7i;MATS8i;GATS1c;GATS2c;GATS3c;GATS4c;GATS5c;GATS6c;GATS7c;GATS8c;GATS1dv;GATS2dv;GATS3dv;GATS4dv;GATS5dv;GATS6dv;GATS7dv;GATS8dv;GATS1d;GATS2d;GATS3d;GATS4d;GATS5d;GATS6d;GATS7d;GATS8d;GATS1s;GATS2s;GATS3s;GATS4s;GATS5s;GATS6s;GATS7s;GATS8s;GATS1Z;GATS2Z;GATS3Z;GATS4Z;GATS5Z;GATS6Z;GATS7Z;GATS8Z;GATS1m;GATS2m;GATS3m;GATS4m;GATS5m;GATS6m;GATS7m;GATS8m;GATS1v;GATS2v;GATS3v;GATS4v;GATS5v;GATS6v;GATS7v;GATS8v;GATS1se;GATS2se;GATS3se;GATS4se;GATS5se;GATS6se;GATS7se;GATS8se;GATS1pe;GATS2pe;GATS3pe;GATS4pe;GATS5pe;GATS6pe;GATS7pe;GATS8pe;GATS1are;GATS2are;GATS3are;GATS4are;GATS5are;GATS6are;GATS7are;GATS8are;GATS1p;GATS2p;GATS3p;GATS4p;GATS5p;GATS6p;GATS7p;GATS8p;GATS1i;GATS2i;GATS3i;GATS4i;GATS5i;GATS6i;GATS7i;GATS8i;BCUTc-1h;BCUTc-1l;BCUTdv-1h;BCUTdv-1l;BCUTd-1h;BCUTd-1l;BCUTs-1h;BCUTs-1l;BCUTZ-1h;BCUTZ-1l;BCUTm-1h;BCUTm-1l;BCUTv-1h;BCUTv-1l;BCUTse-1h;BCUTse-1l;BCUTpe-1h;BCUTpe-1l;BCUTare-1h;BCUTare-1l;BCUTp-1h;BCUTp-1l;BCUTi-1h;BCUTi-1l;BalabanJ;SpAbs_DzZ;SpMax_DzZ;SpDiam_DzZ;SpAD_DzZ;SpMAD_DzZ;LogEE_DzZ;SM1_DzZ;VE1_DzZ;VE2_DzZ;VE3_DzZ;VR1_DzZ;VR2_DzZ;VR3_DzZ;SpAbs_Dzm;SpMax_Dzm;SpDiam_Dzm;SpAD_Dzm;SpMAD_Dzm;LogEE_Dzm;SM1_Dzm;VE1_Dzm;VE2_Dzm;VE3_Dzm;VR1_Dzm;VR2_Dzm;VR3_Dzm;SpAbs_Dzv;SpMax_Dzv;SpDiam_Dzv;SpAD_Dzv;SpMAD_Dzv;LogEE_Dzv;SM1_Dzv;VE1_Dzv;VE2_Dzv;VE3_Dzv;VR1_Dzv;VR2_Dzv;VR3_Dzv;SpAbs_Dzse;SpMax_Dzse;SpDiam_Dzse;SpAD_Dzse;SpMAD_Dzse;LogEE_Dzse;SM1_Dzse;VE1_Dzse;VE2_Dzse;VE3_Dzse;VR1_Dzse;VR2_Dzse;VR3_Dzse;SpAbs_Dzpe;SpMax_Dzpe;SpDiam_Dzpe;SpAD_Dzpe;SpMAD_Dzpe;LogEE_Dzpe;SM1_Dzpe;VE1_Dzpe;VE2_Dzpe;VE3_Dzpe;VR1_Dzpe;VR2_Dzpe;VR3_Dzpe;SpAbs_Dzare;SpMax_Dzare;SpDiam_Dzare;SpAD_Dzare;SpMAD_Dzare;LogEE_Dzare;SM1_Dzare;VE1_Dzare;VE2_Dzare;VE3_Dzare;VR1_Dzare;VR2_Dzare;VR3_Dzare;SpAbs_Dzp;SpMax_Dzp;SpDiam_Dzp;SpAD_Dzp;SpMAD_Dzp;LogEE_Dzp;SM1_Dzp;VE1_Dzp;VE2_Dzp;VE3_Dzp;VR1_Dzp;VR2_Dzp;VR3_Dzp;SpAbs_Dzi;SpMax_Dzi;SpDiam_Dzi;SpAD_Dzi;SpMAD_Dzi;LogEE_Dzi;SM1_Dzi;VE1_Dzi;VE2_Dzi;VE3_Dzi;VR1_Dzi;VR2_Dzi;VR3_Dzi;BertzCT;nBonds;nBondsO;nBondsS;nBondsD;nBondsT;nBondsA;nBondsM;nBondsKS;nBondsKD;RNCG;RPCG;C1SP1;C2SP1;C1SP2;C2SP2;C3SP2;C1SP3;C2SP3;C3SP3;C4SP3;HybRatio;FCSP3;Xch-3d;Xch-4d;Xch-5d;Xch-6d;Xch-7d;Xch-3dv;Xch-4dv;Xch-5dv;Xch-6dv;Xch-7dv;Xc-3d;Xc-4d;Xc-5d;Xc-6d;Xc-3dv;Xc-4dv;Xc-5dv;Xc-6dv;Xpc-4d;Xpc-5d;Xpc-6d;Xpc-4dv;Xpc-5dv;Xpc-6dv;Xp-0d;Xp-1d;Xp-2d;Xp-3d;Xp-4d;Xp-5d;Xp-6d;Xp-7d;AXp-0d;AXp-1d;AXp-2d;AXp-3d;AXp-4d;AXp-5d;AXp-6d;AXp-7d;Xp-0dv;Xp-1dv;Xp-2dv;Xp-3dv;Xp-4dv;Xp-5dv;Xp-6dv;Xp-7dv;AXp-0dv;AXp-1dv;AXp-2dv;AXp-3dv;AXp-4dv;AXp-5dv;AXp-6dv;AXp-7dv;SZ;Sm;Sv;Sse;Spe;Sare;Sp;Si;MZ;Mm;Mv;Mse;Mpe;Mare;Mp;Mi;SpAbs_Dt;SpMax_Dt;SpDiam_Dt;SpAD_Dt;SpMAD_Dt;LogEE_Dt;SM1_Dt;VE1_Dt;VE2_Dt;VE3_Dt;VR1_Dt;VR2_Dt;VR3_Dt;DetourIndex;SpAbs_D;SpMax_D;SpDiam_D;SpAD_D;SpMAD_D;LogEE_D;VE1_D;VE2_D;VE3_D;VR1_D;VR2_D;VR3_D;NsLi;NssBe;NssssBe;NssBH;NsssB;NssssB;NsCH3;NdCH2;NssCH2;NtCH;NdsCH;NaaCH;NsssCH;NddC;NtsC;NdssC;NaasC;NaaaC;NssssC;NsNH3;NsNH2;NssNH2;NdNH;NssNH;NaaNH;NtN;NsssNH;NdsN;NaaN;NsssN;NddsN;NaasN;NssssN;NsOH;NdO;NssO;NaaO;NsF;NsSiH3;NssSiH2;NsssSiH;NssssSi;NsPH2;NssPH;NsssP;NdsssP;NsssssP;NsSH;NdS;NssS;NaaS;NdssS;NddssS;NsCl;NsGeH3;NssGeH2;NsssGeH;NssssGe;NsAsH2;NssAsH;NsssAs;NsssdAs;NsssssAs;NsSeH;NdSe;NssSe;NaaSe;NdssSe;NddssSe;NsBr;NsSnH3;NssSnH2;NsssSnH;NssssSn;NsI;NsPbH3;NssPbH2;NsssPbH;NssssPb;SsLi;SssBe;SssssBe;SssBH;SsssB;SssssB;SsCH3;SdCH2;SssCH2;StCH;SdsCH;SaaCH;SsssCH;SddC;StsC;SdssC;SaasC;SaaaC;SssssC;SsNH3;SsNH2;SssNH2;SdNH;SssNH;SaaNH;StN;SsssNH;SdsN;SaaN;SsssN;SddsN;SaasN;SssssN;SsOH;SdO;SssO;SaaO;SsF;SsSiH3;SssSiH2;SsssSiH;SssssSi;SsPH2;SssPH;SsssP;SdsssP;SsssssP;SsSH;SdS;SssS;SaaS;SdssS;SddssS;SsCl;SsGeH3;SssGeH2;SsssGeH;SssssGe;SsAsH2;SssAsH;SsssAs;SsssdAs;SsssssAs;SsSeH;SdSe;SssSe;SaaSe;SdssSe;SddssSe;SsBr;SsSnH3;SssSnH2;SsssSnH;SssssSn;SsI;SsPbH3;SssPbH2;SsssPbH;SssssPb;MAXsLi;MAXssBe;MAXssssBe;MAXssBH;MAXsssB;MAXssssB;MAXsCH3;MAXdCH2;MAXssCH2;MAXtCH;MAXdsCH;MAXaaCH;MAXsssCH;MAXddC;MAXtsC;MAXdssC;MAXaasC;MAXaaaC;MAXssssC;MAXsNH3;MAXsNH2;MAXssNH2;MAXdNH;MAXssNH;MAXaaNH;MAXtN;MAXsssNH;MAXdsN;MAXaaN;MAXsssN;MAXddsN;MAXaasN;MAXssssN;MAXsOH;MAXdO;MAXssO;MAXaaO;MAXsF;MAXsSiH3;MAXssSiH2;MAXsssSiH;MAXssssSi;MAXsPH2;MAXssPH;MAXsssP;MAXdsssP;MAXsssssP;MAXsSH;MAXdS;MAXssS;MAXaaS;MAXdssS;MAXddssS;MAXsCl;MAXsGeH3;MAXssGeH2;MAXsssGeH;MAXssssGe;MAXsAsH2;MAXssAsH;MAXsssAs;MAXsssdAs;MAXsssssAs;MAXsSeH;MAXdSe;MAXssSe;MAXaaSe;MAXdssSe;MAXddssSe;MAXsBr;MAXsSnH3;MAXssSnH2;MAXsssSnH;MAXssssSn;MAXsI;MAXsPbH3;MAXssPbH2;MAXsssPbH;MAXssssPb;MINsLi;MINssBe;MINssssBe;MINssBH;MINsssB;MINssssB;MINsCH3;MINdCH2;MINssCH2;MINtCH;MINdsCH;MINaaCH;MINsssCH;MINddC;MINtsC;MINdssC;MINaasC;MINaaaC;MINssssC;MINsNH3;MINsNH2;MINssNH2;MINdNH;MINssNH;MINaaNH;MINtN;MINsssNH;MINdsN;MINaaN;MINsssN;MINddsN;MINaasN;MINssssN;MINsOH;MINdO;MINssO;MINaaO;MINsF;MINsSiH3;MINssSiH2;MINsssSiH;MINssssSi;MINsPH2;MINssPH;MINsssP;MINdsssP;MINsssssP;MINsSH;MINdS;MINssS;MINaaS;MINdssS;MINddssS;MINsCl;MINsGeH3;MINssGeH2;MINsssGeH;MINssssGe;MINsAsH2;MINssAsH;MINsssAs;MINsssdAs;MINsssssAs;MINsSeH;MINdSe;MINssSe;MINaaSe;MINdssSe;MINddssSe;MINsBr;MINsSnH3;MINssSnH2;MINsssSnH;MINssssSn;MINsI;MINsPbH3;MINssPbH2;MINsssPbH;MINssssPb;ECIndex;ETA_alpha;AETA_alpha;ETA_shape_p;ETA_shape_y;ETA_shape_x;ETA_beta;AETA_beta;ETA_beta_s;AETA_beta_s;ETA_beta_ns;AETA_beta_ns;ETA_beta_ns_d;AETA_beta_ns_d;ETA_eta;AETA_eta;ETA_eta_L;AETA_eta_L;ETA_eta_R;AETA_eta_R;ETA_eta_RL;AETA_eta_RL;ETA_eta_F;AETA_eta_F;ETA_eta_FL;AETA_eta_FL;ETA_eta_B;AETA_eta_B;ETA_eta_BR;AETA_eta_BR;ETA_dAlpha_A;ETA_dAlpha_B;ETA_epsilon_1;ETA_epsilon_2;ETA_epsilon_3;ETA_epsilon_4;ETA_epsilon_5;ETA_dEpsilon_A;ETA_dEpsilon_B;ETA_dEpsilon_C;ETA_dEpsilon_D;ETA_dBeta;AETA_dBeta;ETA_psi_1;ETA_dPsi_A;ETA_dPsi_B;fragCpx;fMF;nHBAcc;nHBDon;IC0;IC1;IC2;IC3;IC4;IC5;TIC0;TIC1;TIC2;TIC3;TIC4;TIC5;SIC0;SIC1;SIC2;SIC3;SIC4;SIC5;BIC0;BIC1;BIC2;BIC3;BIC4;BIC5;CIC0;CIC1;CIC2;CIC3;CIC4;CIC5;MIC0;MIC1;MIC2;MIC3;MIC4;MIC5;ZMIC0;ZMIC1;ZMIC2;ZMIC3;ZMIC4;ZMIC5;Kier1;Kier2;Kier3;Lipinski;GhoseFilter;FilterItLogS;VMcGowan;LabuteASA;PEOE_VSA1;PEOE_VSA2;PEOE_VSA3;PEOE_VSA4;PEOE_VSA5;PEOE_VSA6;PEOE_VSA7;PEOE_VSA8;PEOE_VSA9;PEOE_VSA10;PEOE_VSA11;PEOE_VSA12;PEOE_VSA13;SMR_VSA1;SMR_VSA2;SMR_VSA3;SMR_VSA4;SMR_VSA5;SMR_VSA6;SMR_VSA7;SMR_VSA8;SMR_VSA9;SlogP_VSA1;SlogP_VSA2;SlogP_VSA3;SlogP_VSA4;SlogP_VSA5;SlogP_VSA6;SlogP_VSA7;SlogP_VSA8;SlogP_VSA9;SlogP_VSA10;SlogP_VSA11;EState_VSA1;EState_VSA2;EState_VSA3;EState_VSA4;EState_VSA5;EState_VSA6;EState_VSA7;EState_VSA8;EState_VSA9;EState_VSA10;VSA_EState1;VSA_EState2;VSA_EState3;VSA_EState4;VSA_EState5;VSA_EState6;VSA_EState7;VSA_EState8;VSA_EState9;MDEC-11;MDEC-12;MDEC-13;MDEC-14;MDEC-22;MDEC-23;MDEC-24;MDEC-33;MDEC-34;MDEC-44;MDEO-11;MDEO-12;MDEO-22;MDEN-11;MDEN-12;MDEN-13;MDEN-22;MDEN-23;MDEN-33;MID;AMID;MID_h;AMID_h;MID_C;AMID_C;MID_N;AMID_N;MID_O;AMID_O;MID_X;AMID_X;MPC2;MPC3;MPC4;MPC5;MPC6;MPC7;MPC8;MPC9;MPC10;TMPC10;piPC1;piPC2;piPC3;piPC4;piPC5;piPC6;piPC7;piPC8;piPC9;piPC10;TpiPC10;apol;bpol;nRing;n3Ring;n4Ring;n5Ring;n6Ring;n7Ring;n8Ring;n9Ring;n10Ring;n11Ring;n12Ring;nG12Ring;nHRing;n3HRing;n4HRing;n5HRing;n6HRing;n7HRing;n8HRing;n9HRing;n10HRing;n11HRing;n12HRing;nG12HRing;naRing;n3aRing;n4aRing;n5aRing;n6aRing;n7aRing;n8aRing;n9aRing;n10aRing;n11aRing;n12aRing;nG12aRing;naHRing;n3aHRing;n4aHRing;n5aHRing;n6aHRing;n7aHRing;n8aHRing;n9aHRing;n10aHRing;n11aHRing;n12aHRing;nG12aHRing;nARing;n3ARing;n4ARing;n5ARing;n6ARing;n7ARing;n8ARing;n9ARing;n10ARing;n11ARing;n12ARing;nG12ARing;nAHRing;n3AHRing;n4AHRing;n5AHRing;n6AHRing;n7AHRing;n8AHRing;n9AHRing;n10AHRing;n11AHRing;n12AHRing;nG12AHRing;nFRing;n4FRing;n5FRing;n6FRing;n7FRing;n8FRing;n9FRing;n10FRing;n11FRing;n12FRing;nG12FRing;nFHRing;n4FHRing;n5FHRing;n6FHRing;n7FHRing;n8FHRing;n9FHRing;n10FHRing;n11FHRing;n12FHRing;nG12FHRing;nFaRing;n4FaRing;n5FaRing;n6FaRing;n7FaRing;n8FaRing;n9FaRing;n10FaRing;n11FaRing;n12FaRing;nG12FaRing;nFaHRing;n4FaHRing;n5FaHRing;n6FaHRing;n7FaHRing;n8FaHRing;n9FaHRing;n10FaHRing;n11FaHRing;n12FaHRing;nG12FaHRing;nFARing;n4FARing;n5FARing;n6FARing;n7FARing;n8FARing;n9FARing;n10FARing;n11FARing;n12FARing;nG12FARing;nFAHRing;n4FAHRing;n5FAHRing;n6FAHRing;n7FAHRing;n8FAHRing;n9FAHRing;n10FAHRing;n11FAHRing;n12FAHRing;nG12FAHRing;nRot;RotRatio;SLogP;SMR;TopoPSA(NO);TopoPSA;GGI1;GGI2;GGI3;GGI4;GGI5;GGI6;GGI7;GGI8;GGI9;GGI10;JGI1;JGI2;JGI3;JGI4;JGI5;JGI6;JGI7;JGI8;JGI9;JGI10;JGT10;Diameter;Radius;TopoShapeIndex;PetitjeanIndex;Vabc;VAdjMat;MWC01;MWC02;MWC03;MWC04;MWC05;MWC06;MWC07;MWC08;MWC09;MWC10;TMWC10;SRW02;SRW03;SRW04;SRW05;SRW06;SRW07;SRW08;SRW09;SRW10;TSRW10;MW;AMW;WPath;WPol;Zagreb1;Zagreb2;mZagreb1;mZagreb2,0,mordred
 mordred_filtered,AATS0Z;AATS0are;AATS0d;AATS0dv;AATS0i;AATS0m;AATS0p;AATS0pe;AATS0s;AATS0se;AATS0v;AATS1Z;AATS1are;AATS1d;AATS1dv;AATS1i;AATS1m;AATS1p;AATS1pe;AATS1s;AATS1se;AATS1v;AATS2Z;AATS2are;AATS2d;AATS2dv;AATS2i;AATS2m;AATS2p;AATS2pe;AATS2s;AATS2se;AATS2v;AATS3Z;AATS3are;AATS3d;AATS3dv;AATS3i;AATS3m;AATS3p;AATS3pe;AATS3s;AATS3se;AATS3v;AATS4Z;AATS4are;AATS4d;AATS4dv;AATS4i;AATS4m;AATS4p;AATS4pe;AATS4s;AATS4se;AATS4v;AATS5Z;AATS5are;AATS5d;AATS5dv;AATS5i;AATS5m;AATS5p;AATS5pe;AATS5s;AATS5se;AATS5v;AATS6Z;AATS6are;AATS6d;AATS6dv;AATS6i;AATS6m;AATS6p;AATS6pe;AATS6s;AATS6se;AATS6v;AATS7Z;AATS7are;AATS7d;AATS7dv;AATS7i;AATS7m;AATS7p;AATS7pe;AATS7s;AATS7se;AATS7v;AATS8Z;AATS8are;AATS8d;AATS8dv;AATS8i;AATS8m;AATS8p;AATS8pe;AATS8s;AATS8se;AATS8v;AATSC0Z;AATSC0are;AATSC0c;AATSC0d;AATSC0dv;AATSC0i;AATSC0m;AATSC0p;AATSC0pe;AATSC0s;AATSC0se;AATSC0v;AATSC1Z;AATSC1are;AATSC1c;AATSC1d;AATSC1dv;AATSC1i;AATSC1m;AATSC1p;AATSC1pe;AATSC1s;AATSC1se;AATSC1v;AATSC2Z;AATSC2are;AATSC2c;AATSC2d;AATSC2dv;AATSC2i;AATSC2m;AATSC2p;AATSC2pe;AATSC2s;AATSC2se;AATSC2v;AATSC3Z;AATSC3are;AATSC3c;AATSC3d;AATSC3dv;AATSC3i;AATSC3m;AATSC3p;AATSC3pe;AATSC3s;AATSC3se;AATSC3v;AATSC4Z;AATSC4are;AATSC4c;AATSC4d;AATSC4dv;AATSC4i;AATSC4m;AATSC4p;AATSC4pe;AATSC4s;AATSC4se;AATSC4v;AATSC5Z;AATSC5are;AATSC5c;AATSC5d;AATSC5dv;AATSC5i;AATSC5m;AATSC5p;AATSC5pe;AATSC5s;AATSC5se;AATSC5v;AATSC6Z;AATSC6are;AATSC6c;AATSC6d;AATSC6dv;AATSC6i;AATSC6m;AATSC6p;AATSC6pe;AATSC6s;AATSC6se;AATSC6v;AATSC7Z;AATSC7are;AATSC7c;AATSC7d;AATSC7dv;AATSC7i;AATSC7m;AATSC7p;AATSC7pe;AATSC7s;AATSC7se;AATSC7v;AATSC8Z;AATSC8are;AATSC8c;AATSC8d;AATSC8dv;AATSC8i;AATSC8m;AATSC8p;AATSC8pe;AATSC8s;AATSC8se;AATSC8v;ABC;ABCGG;AETA_alpha;AETA_beta;AETA_beta_ns;AETA_beta_ns_d;AETA_beta_s;AETA_dBeta;AETA_eta;AETA_eta_B;AETA_eta_BR;AETA_eta_F;AETA_eta_FL;AETA_eta_L;AETA_eta_R;AETA_eta_RL;AMID;AMID_C;AMID_N;AMID_O;AMID_X;AMID_h;AMW;ATS0Z;ATS0are;ATS0d;ATS0dv;ATS0i;ATS0m;ATS0p;ATS0pe;ATS0s;ATS0se;ATS0v;ATS1Z;ATS1are;ATS1d;ATS1dv;ATS1i;ATS1m;ATS1p;ATS1pe;ATS1s;ATS1se;ATS1v;ATS2Z;ATS2are;ATS2d;ATS2dv;ATS2i;ATS2m;ATS2p;ATS2pe;ATS2s;ATS2se;ATS2v;ATS3Z;ATS3are;ATS3d;ATS3dv;ATS3i;ATS3m;ATS3p;ATS3pe;ATS3s;ATS3se;ATS3v;ATS4Z;ATS4are;ATS4d;ATS4dv;ATS4i;ATS4m;ATS4p;ATS4pe;ATS4s;ATS4se;ATS4v;ATS5Z;ATS5are;ATS5d;ATS5dv;ATS5i;ATS5m;ATS5p;ATS5pe;ATS5s;ATS5se;ATS5v;ATS6Z;ATS6are;ATS6d;ATS6dv;ATS6i;ATS6m;ATS6p;ATS6pe;ATS6s;ATS6se;ATS6v;ATS7Z;ATS7are;ATS7d;ATS7dv;ATS7i;ATS7m;ATS7p;ATS7pe;ATS7s;ATS7se;ATS7v;ATS8Z;ATS8are;ATS8d;ATS8dv;ATS8i;ATS8m;ATS8p;ATS8pe;ATS8s;ATS8se;ATS8v;ATSC0Z;ATSC0are;ATSC0c;ATSC0d;ATSC0dv;ATSC0i;ATSC0m;ATSC0p;ATSC0pe;ATSC0s;ATSC0se;ATSC0v;ATSC1Z;ATSC1are;ATSC1c;ATSC1d;ATSC1dv;ATSC1i;ATSC1m;ATSC1p;ATSC1pe;ATSC1s;ATSC1se;ATSC1v;ATSC2Z;ATSC2are;ATSC2c;ATSC2d;ATSC2dv;ATSC2i;ATSC2m;ATSC2p;ATSC2pe;ATSC2s;ATSC2se;ATSC2v;ATSC3Z;ATSC3are;ATSC3c;ATSC3d;ATSC3dv;ATSC3i;ATSC3m;ATSC3p;ATSC3pe;ATSC3s;ATSC3se;ATSC3v;ATSC4Z;ATSC4are;ATSC4c;ATSC4d;ATSC4dv;ATSC4i;ATSC4m;ATSC4p;ATSC4pe;ATSC4s;ATSC4se;ATSC4v;ATSC5Z;ATSC5are;ATSC5c;ATSC5d;ATSC5dv;ATSC5i;ATSC5m;ATSC5p;ATSC5pe;ATSC5s;ATSC5se;ATSC5v;ATSC6Z;ATSC6are;ATSC6c;ATSC6d;ATSC6dv;ATSC6i;ATSC6m;ATSC6p;ATSC6pe;ATSC6s;ATSC6se;ATSC6v;ATSC7Z;ATSC7are;ATSC7c;ATSC7d;ATSC7dv;ATSC7i;ATSC7m;ATSC7p;ATSC7pe;ATSC7s;ATSC7se;ATSC7v;ATSC8Z;ATSC8are;ATSC8c;ATSC8d;ATSC8dv;ATSC8i;ATSC8m;ATSC8p;ATSC8pe;ATSC8s;ATSC8se;ATSC8v;AXp-0d;AXp-0dv;AXp-1d;AXp-1dv;AXp-2d;AXp-2dv;AXp-3d;AXp-3dv;AXp-4d;AXp-4dv;AXp-5d;AXp-5dv;AXp-6d;AXp-6dv;AXp-7d;AXp-7dv;BCUTZ-1h;BCUTZ-1l;BCUTare-1h;BCUTare-1l;BCUTc-1h;BCUTc-1l;BCUTd-1h;BCUTd-1l;BCUTdv-1h;BCUTdv-1l;BCUTi-1h;BCUTi-1l;BCUTm-1h;BCUTm-1l;BCUTp-1h;BCUTp-1l;BCUTpe-1h;BCUTpe-1l;BCUTs-1h;BCUTs-1l;BCUTse-1h;BCUTse-1l;BCUTv-1h;BCUTv-1l;BIC0;BIC1;BIC2;BIC3;BIC4;BIC5;BalabanJ;BertzCT;C1SP1;C1SP2;C1SP3;C2SP1;C2SP2;C2SP3;C3SP2;C3SP3;C4SP3;CIC0;CIC1;CIC2;CIC3;CIC4;CIC5;DPSA1;DPSA2;DPSA3;DPSA4;DPSA5;DetourIndex;Diameter;ECIndex;EState_VSA1;EState_VSA10;EState_VSA2;EState_VSA3;EState_VSA4;EState_VSA5;EState_VSA6;EState_VSA7;EState_VSA8;EState_VSA9;ETA_alpha;ETA_beta;ETA_beta_ns;ETA_beta_ns_d;ETA_beta_s;ETA_dAlpha_A;ETA_dAlpha_B;ETA_dBeta;ETA_dEpsilon_A;ETA_dEpsilon_B;ETA_dEpsilon_C;ETA_dEpsilon_D;ETA_dPsi_A;ETA_dPsi_B;ETA_epsilon_1;ETA_epsilon_2;ETA_epsilon_3;ETA_epsilon_4;ETA_epsilon_5;ETA_eta;ETA_eta_B;ETA_eta_BR;ETA_eta_F;ETA_eta_FL;ETA_eta_L;ETA_eta_R;ETA_eta_RL;ETA_psi_1;ETA_shape_p;ETA_shape_x;ETA_shape_y;FCSP3;FNSA1;FNSA2;FNSA3;FNSA4;FNSA5;FPSA1;FPSA2;FPSA3;FPSA4;FPSA5;FilterItLogS;GATS1Z;GATS1are;GATS1c;GATS1d;GATS1dv;GATS1i;GATS1m;GATS1p;GATS1pe;GATS1s;GATS1se;GATS1v;GATS2Z;GATS2are;GATS2c;GATS2d;GATS2dv;GATS2i;GATS2m;GATS2p;GATS2pe;GATS2s;GATS2se;GATS2v;GATS3Z;GATS3are;GATS3c;GATS3d;GATS3dv;GATS3i;GATS3m;GATS3p;GATS3pe;GATS3s;GATS3se;GATS3v;GATS4Z;GATS4are;GATS4c;GATS4d;GATS4dv;GATS4i;GATS4m;GATS4p;GATS4pe;GATS4s;GATS4se;GATS4v;GATS5Z;GATS5are;GATS5c;GATS5d;GATS5dv;GATS5i;GATS5m;GATS5p;GATS5pe;GATS5s;GATS5se;GATS5v;GATS6Z;GATS6are;GATS6c;GATS6d;GATS6dv;GATS6i;GATS6m;GATS6p;GATS6pe;GATS6s;GATS6se;GATS6v;GATS7Z;GATS7are;GATS7c;GATS7d;GATS7dv;GATS7i;GATS7m;GATS7p;GATS7pe;GATS7s;GATS7se;GATS7v;GATS8Z;GATS8are;GATS8c;GATS8d;GATS8dv;GATS8i;GATS8m;GATS8p;GATS8pe;GATS8s;GATS8se;GATS8v;GGI1;GGI10;GGI2;GGI3;GGI4;GGI5;GGI6;GGI7;GGI8;GGI9;GRAV;GRAVH;GRAVHp;GRAVp;GeomDiameter;GeomPetitjeanIndex;GeomRadius;GeomShapeIndex;GhoseFilter;HybRatio;IC0;IC1;IC2;IC3;IC4;IC5;JGI1;JGI10;JGI2;JGI3;JGI4;JGI5;JGI6;JGI7;JGI8;JGI9;JGT10;Kier1;Kier2;Kier3;LabuteASA;Lipinski;LogEE_A;LogEE_D;LogEE_Dt;LogEE_DzZ;LogEE_Dzare;LogEE_Dzi;LogEE_Dzm;LogEE_Dzp;LogEE_Dzpe;LogEE_Dzse;LogEE_Dzv;MATS1Z;MATS1are;MATS1c;MATS1d;MATS1dv;MATS1i;MATS1m;MATS1p;MATS1pe;MATS1s;MATS1se;MATS1v;MATS2Z;MATS2are;MATS2c;MATS2d;MATS2dv;MATS2i;MATS2m;MATS2p;MATS2pe;MATS2s;MATS2se;MATS2v;MATS3Z;MATS3are;MATS3c;MATS3d;MATS3dv;MATS3i;MATS3m;MATS3p;MATS3pe;MATS3s;MATS3se;MATS3v;MATS4Z;MATS4are;MATS4c;MATS4d;MATS4dv;MATS4i;MATS4m;MATS4p;MATS4pe;MATS4s;MATS4se;MATS4v;MATS5Z;MATS5are;MATS5c;MATS5d;MATS5dv;MATS5i;MATS5m;MATS5p;MATS5pe;MATS5s;MATS5se;MATS5v;MATS6Z;MATS6are;MATS6c;MATS6d;MATS6dv;MATS6i;MATS6m;MATS6p;MATS6pe;MATS6s;MATS6se;MATS6v;MATS7Z;MATS7are;MATS7c;MATS7d;MATS7dv;MATS7i;MATS7m;MATS7p;MATS7pe;MATS7s;MATS7se;MATS7v;MATS8Z;MATS8are;MATS8c;MATS8d;MATS8dv;MATS8i;MATS8m;MATS8p;MATS8pe;MATS8s;MATS8se;MATS8v;MDEC-22;MDEC-23;MDEC-33;MIC0;MIC1;MIC2;MIC3;MIC4;MIC5;MID;MID_C;MID_N;MID_O;MID_X;MID_h;MOMI-X;MOMI-Y;MOMI-Z;MPC10;MPC2;MPC3;MPC4;MPC5;MPC6;MPC7;MPC8;MPC9;MW;MWC01;MWC02;MWC03;MWC04;MWC05;MWC06;MWC07;MWC08;MWC09;MWC10;MZ;Mare;Mi;Mm;Mor01;Mor01m;Mor01p;Mor01se;Mor01v;Mor02;Mor02m;Mor02p;Mor02se;Mor02v;Mor03;Mor03m;Mor03p;Mor03se;Mor03v;Mor04;Mor04m;Mor04p;Mor04se;Mor04v;Mor05;Mor05m;Mor05p;Mor05se;Mor05v;Mor06;Mor06m;Mor06p;Mor06se;Mor06v;Mor07;Mor07m;Mor07p;Mor07se;Mor07v;Mor08;Mor08m;Mor08p;Mor08se;Mor08v;Mor09;Mor09m;Mor09p;Mor09se;Mor09v;Mor10;Mor10m;Mor10p;Mor10se;Mor10v;Mor11;Mor11m;Mor11p;Mor11se;Mor11v;Mor12;Mor12m;Mor12p;Mor12se;Mor12v;Mor13;Mor13m;Mor13p;Mor13se;Mor13v;Mor14;Mor14m;Mor14p;Mor14se;Mor14v;Mor15;Mor15m;Mor15p;Mor15se;Mor15v;Mor16;Mor16m;Mor16p;Mor16se;Mor16v;Mor17;Mor17m;Mor17p;Mor17se;Mor17v;Mor18;Mor18m;Mor18p;Mor18se;Mor18v;Mor19;Mor19m;Mor19p;Mor19se;Mor19v;Mor20;Mor20m;Mor20p;Mor20se;Mor20v;Mor21;Mor21m;Mor21p;Mor21se;Mor21v;Mor22;Mor22m;Mor22p;Mor22se;Mor22v;Mor23;Mor23m;Mor23p;Mor23se;Mor23v;Mor24;Mor24m;Mor24p;Mor24se;Mor24v;Mor25;Mor25m;Mor25p;Mor25se;Mor25v;Mor26;Mor26m;Mor26p;Mor26se;Mor26v;Mor27;Mor27m;Mor27p;Mor27se;Mor27v;Mor28;Mor28m;Mor28p;Mor28se;Mor28v;Mor29;Mor29m;Mor29p;Mor29se;Mor29v;Mor30;Mor30m;Mor30p;Mor30se;Mor30v;Mor31;Mor31m;Mor31p;Mor31se;Mor31v;Mor32;Mor32m;Mor32p;Mor32se;Mor32v;Mp;Mpe;Mse;Mv;NaaCH;NaaN;NaaNH;NaaO;NaaS;NaaaC;NaasC;NaasN;NdCH2;NdO;NdS;NddsN;NddssS;NdsCH;NdsN;NdssC;NsBr;NsCH3;NsCl;NsF;NsNH2;NsOH;NssCH2;NssNH;NssO;NssS;NsssCH;NsssN;NssssC;NtN;NtsC;PEOE_VSA1;PEOE_VSA10;PEOE_VSA11;PEOE_VSA12;PEOE_VSA13;PEOE_VSA2;PEOE_VSA3;PEOE_VSA4;PEOE_VSA5;PEOE_VSA6;PEOE_VSA7;PEOE_VSA8;PEOE_VSA9;PNSA1;PNSA2;PNSA3;PNSA4;PNSA5;PPSA1;PPSA2;PPSA3;PPSA4;PPSA5;PetitjeanIndex;RASA;RNCG;RNCS;RPCG;RPCS;RPSA;Radius;RotRatio;SIC0;SIC1;SIC2;SIC3;SIC4;SIC5;SLogP;SM1_Dt;SM1_DzZ;SM1_Dzare;SM1_Dzi;SM1_Dzm;SM1_Dzp;SM1_Dzpe;SM1_Dzse;SM1_Dzv;SMR;SMR_VSA1;SMR_VSA2;SMR_VSA3;SMR_VSA4;SMR_VSA5;SMR_VSA6;SMR_VSA7;SMR_VSA8;SMR_VSA9;SRW02;SRW03;SRW04;SRW05;SRW06;SRW07;SRW08;SRW09;SRW10;SZ;SaaCH;SaaN;SaaNH;SaaO;SaaS;SaaaC;SaasC;SaasN;Sare;SdCH2;SdO;SdS;SddsN;SddssS;SdsCH;SdsN;SdssC;Si;SlogP_VSA1;SlogP_VSA10;SlogP_VSA11;SlogP_VSA2;SlogP_VSA3;SlogP_VSA4;SlogP_VSA5;SlogP_VSA6;SlogP_VSA7;SlogP_VSA8;SlogP_VSA9;Sm;Sp;SpAD_A;SpAD_D;SpAD_Dt;SpAD_DzZ;SpAD_Dzare;SpAD_Dzi;SpAD_Dzm;SpAD_Dzp;SpAD_Dzpe;SpAD_Dzse;SpAD_Dzv;SpAbs_A;SpAbs_D;SpAbs_Dt;SpAbs_DzZ;SpAbs_Dzare;SpAbs_Dzi;SpAbs_Dzm;SpAbs_Dzp;SpAbs_Dzpe;SpAbs_Dzse;SpAbs_Dzv;SpDiam_A;SpDiam_D;SpDiam_Dt;SpDiam_DzZ;SpDiam_Dzare;SpDiam_Dzi;SpDiam_Dzm;SpDiam_Dzp;SpDiam_Dzpe;SpDiam_Dzse;SpDiam_Dzv;SpMAD_A;SpMAD_D;SpMAD_Dt;SpMAD_DzZ;SpMAD_Dzare;SpMAD_Dzi;SpMAD_Dzm;SpMAD_Dzp;SpMAD_Dzpe;SpMAD_Dzse;SpMAD_Dzv;SpMax_A;SpMax_D;SpMax_Dt;SpMax_DzZ;SpMax_Dzare;SpMax_Dzi;SpMax_Dzm;SpMax_Dzp;SpMax_Dzpe;SpMax_Dzse;SpMax_Dzv;Spe;SsBr;SsCH3;SsCl;SsF;SsNH2;SsOH;Sse;SssCH2;SssNH;SssO;SssS;SsssCH;SsssN;SssssC;StN;StsC;Sv;TASA;TIC0;TIC1;TIC2;TIC3;TIC4;TIC5;TMPC10;TMWC10;TPSA;TSRW10;TopoPSA;TopoPSA(NO);TopoShapeIndex;TpiPC10;VAdjMat;VE1_A;VE1_D;VE1_Dt;VE1_DzZ;VE1_Dzare;VE1_Dzi;VE1_Dzm;VE1_Dzp;VE1_Dzpe;VE1_Dzse;VE1_Dzv;VE2_A;VE2_D;VE2_Dt;VE2_DzZ;VE2_Dzare;VE2_Dzi;VE2_Dzm;VE2_Dzp;VE2_Dzpe;VE2_Dzse;VE2_Dzv;VE3_A;VE3_D;VE3_Dt;VE3_DzZ;VE3_Dzare;VE3_Dzi;VE3_Dzm;VE3_Dzp;VE3_Dzpe;VE3_Dzse;VE3_Dzv;VMcGowan;VR1_A;VR1_D;VR1_Dt;VR1_DzZ;VR1_Dzare;VR1_Dzi;VR1_Dzm;VR1_Dzp;VR1_Dzpe;VR1_Dzse;VR1_Dzv;VR2_A;VR2_D;VR2_Dt;VR2_DzZ;VR2_Dzare;VR2_Dzi;VR2_Dzm;VR2_Dzp;VR2_Dzpe;VR2_Dzse;VR2_Dzv;VR3_A;VR3_D;VR3_Dt;VR3_DzZ;VR3_Dzare;VR3_Dzi;VR3_Dzm;VR3_Dzp;VR3_Dzpe;VR3_Dzse;VR3_Dzv;VSA_EState1;VSA_EState2;VSA_EState3;VSA_EState4;VSA_EState5;VSA_EState6;VSA_EState7;VSA_EState8;VSA_EState9;Vabc;WNSA1;WNSA2;WNSA3;WNSA4;WNSA5;WPSA1;WPSA2;WPSA3;WPSA4;WPSA5;WPath;WPol;Xc-3d;Xc-3dv;Xc-4d;Xc-4dv;Xc-5d;Xc-5dv;Xc-6d;Xc-6dv;Xch-3d;Xch-3dv;Xch-4d;Xch-4dv;Xch-5d;Xch-5dv;Xch-6d;Xch-6dv;Xch-7d;Xch-7dv;Xp-0d;Xp-0dv;Xp-1d;Xp-1dv;Xp-2d;Xp-2dv;Xp-3d;Xp-3dv;Xp-4d;Xp-4dv;Xp-5d;Xp-5dv;Xp-6d;Xp-6dv;Xp-7d;Xp-7dv;Xpc-4d;Xpc-4dv;Xpc-5d;Xpc-5dv;Xpc-6d;Xpc-6dv;ZMIC0;ZMIC1;ZMIC2;ZMIC3;ZMIC4;ZMIC5;Zagreb1;Zagreb2;apol;bpol;fMF;fragCpx;mZagreb1;mZagreb2;n10AHRing;n10ARing;n10FAHRing;n10FARing;n10FHRing;n10FRing;n10FaHRing;n10FaRing;n10HRing;n10Ring;n10aHRing;n10aRing;n11AHRing;n11ARing;n11FAHRing;n11FARing;n11FHRing;n11FRing;n11FaHRing;n11FaRing;n11HRing;n11Ring;n11aHRing;n11aRing;n12AHRing;n12ARing;n12FAHRing;n12FARing;n12FHRing;n12FRing;n12FaHRing;n12FaRing;n12HRing;n12Ring;n12aHRing;n12aRing;n3AHRing;n3ARing;n3HRing;n3Ring;n3aHRing;n3aRing;n4AHRing;n4ARing;n4FAHRing;n4FARing;n4FHRing;n4FRing;n4FaHRing;n4FaRing;n4HRing;n4Ring;n4aHRing;n4aRing;n5AHRing;n5ARing;n5FAHRing;n5FARing;n5FHRing;n5FRing;n5FaHRing;n5FaRing;n5HRing;n5Ring;n5aHRing;n5aRing;n6AHRing;n6ARing;n6FAHRing;n6FARing;n6FHRing;n6FRing;n6FaHRing;n6FaRing;n6HRing;n6Ring;n6aHRing;n6aRing;n7AHRing;n7ARing;n7FAHRing;n7FARing;n7FHRing;n7FRing;n7FaHRing;n7FaRing;n7HRing;n7Ring;n7aHRing;n7aRing;n8AHRing;n8ARing;n8FAHRing;n8FARing;n8FHRing;n8FRing;n8FaHRing;n8FaRing;n8HRing;n8Ring;n8aHRing;n8aRing;n9AHRing;n9ARing;n9FAHRing;n9FARing;n9FHRing;n9FRing;n9FaHRing;n9FaRing;n9HRing;n9Ring;n9aHRing;n9aRing;nAHRing;nARing;nAcid;nAromAtom;nAromBond;nAtom;nB;nBase;nBonds;nBondsA;nBondsD;nBondsKD;nBondsKS;nBondsM;nBondsO;nBondsS;nBondsT;nBr;nBridgehead;nC;nCl;nF;nFAHRing;nFARing;nFHRing;nFRing;nFaHRing;nFaRing;nG12AHRing;nG12ARing;nG12FAHRing;nG12FARing;nG12FHRing;nG12FRing;nG12FaHRing;nG12FaRing;nG12HRing;nG12Ring;nG12aHRing;nG12aRing;nH;nHBAcc;nHBDon;nHRing;nHeavyAtom;nHetero;nI;nN;nO;nP;nRing;nRot;nS;nSpiro;nX;naHRing;naRing;piPC1;piPC10;piPC2;piPC3;piPC4;piPC5;piPC6;piPC7;piPC8;piPC9,0,mordred
 rdkit_raw,MaxEStateIndex;MinEStateIndex;MaxAbsEStateIndex;MinAbsEStateIndex;qed;MolWt;HeavyAtomMolWt;ExactMolWt;NumValenceElectrons;NumRadicalElectrons;MaxPartialCharge;MinPartialCharge;MaxAbsPartialCharge;MinAbsPartialCharge;FpDensityMorgan1;FpDensityMorgan2;FpDensityMorgan3;BalabanJ;BertzCT;Chi0;Chi0n;Chi0v;Chi1;Chi1n;Chi1v;Chi2n;Chi2v;Chi3n;Chi3v;Chi4n;Chi4v;HallKierAlpha;Ipc;Kappa1;Kappa2;Kappa3;LabuteASA;PEOE_VSA1;PEOE_VSA10;PEOE_VSA11;PEOE_VSA12;PEOE_VSA13;PEOE_VSA14;PEOE_VSA2;PEOE_VSA3;PEOE_VSA4;PEOE_VSA5;PEOE_VSA6;PEOE_VSA7;PEOE_VSA8;PEOE_VSA9;SMR_VSA1;SMR_VSA10;SMR_VSA2;SMR_VSA3;SMR_VSA4;SMR_VSA5;SMR_VSA6;SMR_VSA7;SMR_VSA8;SMR_VSA9;SlogP_VSA1;SlogP_VSA10;SlogP_VSA11;SlogP_VSA12;SlogP_VSA2;SlogP_VSA3;SlogP_VSA4;SlogP_VSA5;SlogP_VSA6;SlogP_VSA7;SlogP_VSA8;SlogP_VSA9;TPSA;EState_VSA1;EState_VSA10;EState_VSA11;EState_VSA2;EState_VSA3;EState_VSA4;EState_VSA5;EState_VSA6;EState_VSA7;EState_VSA8;EState_VSA9;VSA_EState1;VSA_EState10;VSA_EState2;VSA_EState3;VSA_EState4;VSA_EState5;VSA_EState6;VSA_EState7;VSA_EState8;VSA_EState9;FractionCSP3;HeavyAtomCount;NHOHCount;NOCount;NumAliphaticCarbocycles;NumAliphaticHeterocycles;NumAliphaticRings;NumAromaticCarbocycles;NumAromaticHeterocycles;NumAromaticRings;NumHAcceptors;NumHDonors;NumHeteroatoms;NumRotatableBonds;NumSaturatedCarbocycles;NumSaturatedHeterocycles;NumSaturatedRings;RingCount;MolLogP;MolMR;fr_Al_COO;fr_Al_OH;fr_Al_OH_noTert;fr_ArN;fr_Ar_COO;fr_Ar_N;fr_Ar_NH;fr_Ar_OH;fr_COO;fr_COO2;fr_C_O;fr_C_O_noCOO;fr_C_S;fr_HOCCN;fr_Imine;fr_NH0;fr_NH1;fr_NH2;fr_N_O;fr_Ndealkylation1;fr_Ndealkylation2;fr_Nhpyrrole;fr_SH;fr_aldehyde;fr_alkyl_carbamate;fr_alkyl_halide;fr_allylic_oxid;fr_amide;fr_amidine;fr_aniline;fr_aryl_methyl;fr_azide;fr_azo;fr_barbitur;fr_benzene;fr_benzodiazepine;fr_bicyclic;fr_diazo;fr_dihydropyridine;fr_epoxide;fr_ester;fr_ether;fr_furan;fr_guanido;fr_halogen;fr_hdrzine;fr_hdrzone;fr_imidazole;fr_imide;fr_isocyan;fr_isothiocyan;fr_ketone;fr_ketone_Topliss;fr_lactam;fr_lactone;fr_methoxy;fr_morpholine;fr_nitrile;fr_nitro;fr_nitro_arom;fr_nitro_arom_nonortho;fr_nitroso;fr_oxazole;fr_oxime;fr_para_hydroxylation;fr_phenol;fr_phenol_noOrthoHbond;fr_phos_acid;fr_phos_ester;fr_piperdine;fr_piperzine;fr_priamide;fr_prisulfonamd;fr_pyridine;fr_quatN;fr_sulfide;fr_sulfonamd;fr_sulfone;fr_term_acetylene;fr_tetrazole;fr_thiazole;fr_thiocyan;fr_thiophene;fr_unbrch_alkane;fr_urea,0,rdkit
+rdkit_scaled,MaxEStateIndex_per_heavyatom;MinEStateIndex_per_heavyatom;MaxAbsEStateIndex_per_heavyatom;MinAbsEStateIndex;qed_per_heavyatom;MolWt_per_heavyatom;HeavyAtomMolWt_per_heavyatom;ExactMolWt_per_heavyatom;NumValenceElectrons;NumRadicalElectrons;MaxPartialCharge;MinPartialCharge;MaxAbsPartialCharge;MinAbsPartialCharge;FpDensityMorgan1_per_heavyatom;FpDensityMorgan2_per_heavyatom;FpDensityMorgan3_per_heavyatom;BalabanJ;BertzCT_per_heavyatom;Chi0_per_heavyatom;Chi0n_per_heavyatom;Chi0v_per_heavyatom;Chi1_per_heavyatom;Chi1n_per_heavyatom;Chi1v_per_heavyatom;Chi2n_per_heavyatom;Chi2v_per_heavyatom;Chi3n_per_heavyatom;Chi3v_per_heavyatom;Chi4n_per_heavyatom;Chi4v_per_heavyatom;HallKierAlpha_per_heavyatom;AvgIpc;Kappa1_per_heavyatom;Kappa2_per_heavyatom;Kappa3;LabuteASA_per_heavyatom;PEOE_VSA1_per_heavyatom;PEOE_VSA10_per_heavyatom;PEOE_VSA11_per_heavyatom;PEOE_VSA12_per_heavyatom;PEOE_VSA13;PEOE_VSA14;PEOE_VSA2_per_heavyatom;PEOE_VSA3;PEOE_VSA4;PEOE_VSA5;PEOE_VSA6_per_heavyatom;PEOE_VSA7_per_heavyatom;PEOE_VSA8_per_heavyatom;PEOE_VSA9_per_heavyatom;SMR_VSA1_per_heavyatom;SMR_VSA10_per_heavyatom;SMR_VSA2;SMR_VSA3_per_heavyatom;SMR_VSA4;SMR_VSA5_per_heavyatom;SMR_VSA6;SMR_VSA7;SMR_VSA8;SMR_VSA9;SlogP_VSA1_per_heavyatom;SlogP_VSA10;SlogP_VSA11;SlogP_VSA12;SlogP_VSA2_per_heavyatom;SlogP_VSA3_per_heavyatom;SlogP_VSA4;SlogP_VSA5_per_heavyatom;SlogP_VSA6;SlogP_VSA7;SlogP_VSA8;SlogP_VSA9;TPSA_per_heavyatom;EState_VSA1_per_heavyatom;EState_VSA10_per_heavyatom;EState_VSA11_per_heavyatom;EState_VSA2;EState_VSA3;EState_VSA4;EState_VSA5;EState_VSA6;EState_VSA7;EState_VSA8;EState_VSA9_per_heavyatom;VSA_EState1_per_heavyatom;VSA_EState10_per_heavyatom;VSA_EState2_per_heavyatom;VSA_EState3_per_heavyatom;VSA_EState4_per_heavyatom;VSA_EState5_per_heavyatom;VSA_EState6_per_heavyatom;VSA_EState7_per_heavyatom;VSA_EState8_per_heavyatom;VSA_EState9_per_heavyatom;FractionCSP3;HeavyAtomCount;NHOHCount;NOCount;NumAliphaticCarbocycles;NumAliphaticHeterocycles;NumAliphaticRings;NumAromaticCarbocycles;NumAromaticHeterocycles;NumAromaticRings;NumHAcceptors;NumHDonors;NumHeteroatoms;NumRotatableBonds;NumSaturatedCarbocycles;NumSaturatedHeterocycles;NumSaturatedRings;RingCount;MolLogP;MolMR_per_heavyatom;fr_Al_COO;fr_Al_OH;fr_Al_OH_noTert;fr_ArN;fr_Ar_COO;fr_Ar_N;fr_Ar_NH;fr_Ar_OH;fr_COO;fr_COO2;fr_C_O;fr_C_O_noCOO;fr_C_S;fr_HOCCN;fr_Imine;fr_NH0;fr_NH1;fr_NH2;fr_N_O;fr_Ndealkylation1;fr_Ndealkylation2;fr_Nhpyrrole;fr_SH;fr_aldehyde;fr_alkyl_carbamate;fr_alkyl_halide;fr_allylic_oxid;fr_amide;fr_amidine;fr_aniline;fr_aryl_methyl;fr_azide;fr_azo;fr_barbitur;fr_benzene;fr_benzodiazepine;fr_bicyclic;fr_diazo;fr_dihydropyridine;fr_epoxide;fr_ester;fr_ether;fr_furan;fr_guanido;fr_halogen;fr_hdrzine;fr_hdrzone;fr_imidazole;fr_imide;fr_isocyan;fr_isothiocyan;fr_ketone;fr_ketone_Topliss;fr_lactam;fr_lactone;fr_methoxy;fr_morpholine;fr_nitrile;fr_nitro;fr_nitro_arom;fr_nitro_arom_nonortho;fr_nitroso;fr_oxazole;fr_oxime;fr_para_hydroxylation;fr_phenol;fr_phenol_noOrthoHbond;fr_phos_acid;fr_phos_ester;fr_piperdine;fr_piperzine;fr_priamide;fr_prisulfonamd;fr_pyridine;fr_quatN;fr_sulfide;fr_sulfonamd;fr_sulfone;fr_term_acetylene;fr_tetrazole;fr_thiazole;fr_thiocyan;fr_thiophene;fr_unbrch_alkane;fr_urea,1,rdkit
+mordred_filtered_scaled,AATS0Z;AATS0are;AATS0d;AATS0dv;AATS0i;AATS0m;AATS0p;AATS0pe;AATS0s;AATS0se;AATS0v;AATS1Z;AATS1are;AATS1d;AATS1dv;AATS1i;AATS1m;AATS1p;AATS1pe;AATS1s;AATS1se;AATS1v;AATS2Z;AATS2are;AATS2d;AATS2dv;AATS2i;AATS2m;AATS2p;AATS2pe;AATS2s;AATS2se;AATS2v;AATS3Z;AATS3are;AATS3d;AATS3dv;AATS3i;AATS3m;AATS3p;AATS3pe;AATS3s;AATS3se;AATS3v;AATS4Z;AATS4are;AATS4d;AATS4dv;AATS4i;AATS4m;AATS4p;AATS4pe;AATS4s;AATS4se;AATS4v;AATS5Z;AATS5are;AATS5d;AATS5dv;AATS5i;AATS5m;AATS5p;AATS5pe;AATS5s;AATS5se;AATS5v;AATS6Z;AATS6are;AATS6d;AATS6dv;AATS6i;AATS6m;AATS6p;AATS6pe;AATS6s;AATS6se;AATS6v;AATS7Z;AATS7are;AATS7d;AATS7dv;AATS7i;AATS7m;AATS7p;AATS7pe;AATS7s;AATS7se;AATS7v;AATS8Z;AATS8are;AATS8d;AATS8dv;AATS8i;AATS8m;AATS8p;AATS8pe;AATS8s;AATS8se;AATS8v;AATSC0Z;AATSC0are;AATSC0c;AATSC0d;AATSC0dv;AATSC0i;AATSC0m;AATSC0p;AATSC0pe;AATSC0s;AATSC0se;AATSC0v;AATSC1Z;AATSC1are;AATSC1c;AATSC1d;AATSC1dv;AATSC1i;AATSC1m;AATSC1p;AATSC1pe;AATSC1s;AATSC1se;AATSC1v;AATSC2Z;AATSC2are;AATSC2c;AATSC2d;AATSC2dv;AATSC2i;AATSC2m;AATSC2p;AATSC2pe;AATSC2s;AATSC2se;AATSC2v;AATSC3Z;AATSC3are;AATSC3c;AATSC3d;AATSC3dv;AATSC3i;AATSC3m;AATSC3p;AATSC3pe;AATSC3s;AATSC3se;AATSC3v;AATSC4Z;AATSC4are;AATSC4c;AATSC4d;AATSC4dv;AATSC4i;AATSC4m;AATSC4p;AATSC4pe;AATSC4s;AATSC4se;AATSC4v;AATSC5Z;AATSC5are;AATSC5c;AATSC5d;AATSC5dv;AATSC5i;AATSC5m;AATSC5p;AATSC5pe;AATSC5s;AATSC5se;AATSC5v;AATSC6Z;AATSC6are;AATSC6c;AATSC6d;AATSC6dv;AATSC6i;AATSC6m;AATSC6p;AATSC6pe;AATSC6s;AATSC6se;AATSC6v;AATSC7Z;AATSC7are;AATSC7c;AATSC7d;AATSC7dv;AATSC7i;AATSC7m;AATSC7p;AATSC7pe;AATSC7s;AATSC7se;AATSC7v;AATSC8Z;AATSC8are;AATSC8c;AATSC8d;AATSC8dv;AATSC8i;AATSC8m;AATSC8p;AATSC8pe;AATSC8s;AATSC8se;AATSC8v;ABC;ABCGG;AETA_alpha;AETA_beta;AETA_beta_ns;AETA_beta_ns_d;AETA_beta_s;AETA_dBeta;AETA_eta_per_heavyatom;AETA_eta_B;AETA_eta_BR;AETA_eta_F_per_heavyatom;AETA_eta_FL;AETA_eta_L;AETA_eta_R_per_heavyatom;AETA_eta_RL;AMID;AMID_C;AMID_N;AMID_O;AMID_X;AMID_h;AMW;ATS0Z;ATS0are_per_heavyatom;ATS0d;ATS0dv_per_heavyatom;ATS0i_per_heavyatom;ATS0m_per_heavyatom;ATS0p_per_heavyatom;ATS0pe_per_heavyatom;ATS0s_per_heavyatom;ATS0se_per_heavyatom;ATS0v_per_heavyatom;ATS1Z;ATS1are_per_heavyatom;ATS1d;ATS1dv_per_heavyatom;ATS1i_per_heavyatom;ATS1m_per_heavyatom;ATS1p_per_heavyatom;ATS1pe_per_heavyatom;ATS1s_per_heavyatom;ATS1se_per_heavyatom;ATS1v_per_heavyatom;ATS2Z;ATS2are_per_heavyatom;ATS2d;ATS2dv_per_heavyatom;ATS2i_per_heavyatom;ATS2m_per_heavyatom;ATS2p_per_heavyatom;ATS2pe_per_heavyatom;ATS2s_per_heavyatom;ATS2se_per_heavyatom;ATS2v_per_heavyatom;ATS3Z;ATS3are_per_heavyatom;ATS3d;ATS3dv_per_heavyatom;ATS3i_per_heavyatom;ATS3m_per_heavyatom;ATS3p_per_heavyatom;ATS3pe_per_heavyatom;ATS3s_per_heavyatom;ATS3se_per_heavyatom;ATS3v_per_heavyatom;ATS4Z;ATS4are_per_heavyatom;ATS4d;ATS4dv_per_heavyatom;ATS4i_per_heavyatom;ATS4m_per_heavyatom;ATS4p_per_heavyatom;ATS4pe_per_heavyatom;ATS4s_per_heavyatom;ATS4se_per_heavyatom;ATS4v_per_heavyatom;ATS5Z;ATS5are_per_heavyatom;ATS5d;ATS5dv_per_heavyatom;ATS5i_per_heavyatom;ATS5m_per_heavyatom;ATS5p_per_heavyatom;ATS5pe_per_heavyatom;ATS5s_per_heavyatom;ATS5se_per_heavyatom;ATS5v_per_heavyatom;ATS6Z;ATS6are_per_heavyatom;ATS6d;ATS6dv_per_heavyatom;ATS6i_per_heavyatom;ATS6m_per_heavyatom;ATS6p_per_heavyatom;ATS6pe_per_heavyatom;ATS6s_per_heavyatom;ATS6se_per_heavyatom;ATS6v_per_heavyatom;ATS7Z;ATS7are_per_heavyatom;ATS7d;ATS7dv_per_heavyatom;ATS7i_per_heavyatom;ATS7m_per_heavyatom;ATS7p_per_heavyatom;ATS7pe_per_heavyatom;ATS7s_per_heavyatom;ATS7se_per_heavyatom;ATS7v_per_heavyatom;ATS8Z;ATS8are_per_heavyatom;ATS8d;ATS8dv_per_heavyatom;ATS8i_per_heavyatom;ATS8m_per_heavyatom;ATS8p_per_heavyatom;ATS8pe_per_heavyatom;ATS8s_per_heavyatom;ATS8se_per_heavyatom;ATS8v_per_heavyatom;ATSC0Z;ATSC0are_per_heavyatom;ATSC0c_per_heavyatom;ATSC0d_per_heavyatom;ATSC0dv_per_heavyatom;ATSC0i_per_heavyatom;ATSC0m;ATSC0p_per_heavyatom;ATSC0pe_per_heavyatom;ATSC0s_per_heavyatom;ATSC0se_per_heavyatom;ATSC0v_per_heavyatom;ATSC1Z;ATSC1are;ATSC1c_per_heavyatom;ATSC1d_per_heavyatom;ATSC1dv_per_heavyatom;ATSC1i;ATSC1m;ATSC1p;ATSC1pe;ATSC1s;ATSC1se;ATSC1v;ATSC2Z;ATSC2are;ATSC2c;ATSC2d;ATSC2dv;ATSC2i;ATSC2m;ATSC2p;ATSC2pe;ATSC2s;ATSC2se;ATSC2v;ATSC3Z;ATSC3are;ATSC3c_per_heavyatom;ATSC3d;ATSC3dv;ATSC3i;ATSC3m;ATSC3p;ATSC3pe;ATSC3s;ATSC3se;ATSC3v;ATSC4Z;ATSC4are;ATSC4c_per_heavyatom;ATSC4d;ATSC4dv;ATSC4i;ATSC4m;ATSC4p;ATSC4pe;ATSC4s;ATSC4se;ATSC4v;ATSC5Z;ATSC5are;ATSC5c;ATSC5d;ATSC5dv;ATSC5i;ATSC5m;ATSC5p;ATSC5pe;ATSC5s;ATSC5se;ATSC5v;ATSC6Z;ATSC6are;ATSC6c;ATSC6d;ATSC6dv;ATSC6i;ATSC6m;ATSC6p;ATSC6pe;ATSC6s;ATSC6se;ATSC6v;ATSC7Z;ATSC7are;ATSC7c_per_heavyatom;ATSC7d;ATSC7dv;ATSC7i;ATSC7m;ATSC7p;ATSC7pe;ATSC7s;ATSC7se;ATSC7v;ATSC8Z;ATSC8are;ATSC8c;ATSC8d;ATSC8dv;ATSC8i;ATSC8m;ATSC8p;ATSC8pe;ATSC8s;ATSC8se;ATSC8v;AXp-0d;AXp-0dv;AXp-1d;AXp-1dv;AXp-2d;AXp-2dv;AXp-3d;AXp-3dv;AXp-4d;AXp-4dv;AXp-5d;AXp-5dv;AXp-6d;AXp-6dv;AXp-7d;AXp-7dv;BCUTZ-1h;BCUTZ-1l;BCUTare-1h;BCUTare-1l;BCUTc-1h;BCUTc-1l;BCUTd-1h;BCUTd-1l;BCUTdv-1h;BCUTdv-1l;BCUTi-1h;BCUTi-1l;BCUTm-1h;BCUTm-1l;BCUTp-1h;BCUTp-1l;BCUTpe-1h;BCUTpe-1l;BCUTs-1h;BCUTs-1l;BCUTse-1h;BCUTse-1l;BCUTv-1h;BCUTv-1l;BIC0_per_heavyatom;BIC1_per_heavyatom;BIC2;BIC3;BIC4;BIC5;BalabanJ;BertzCT_per_heavyatom;C1SP1;C1SP2;C1SP3;C2SP1;C2SP2;C2SP3;C3SP2;C3SP3;C4SP3;CIC0_per_heavyatom;CIC1_per_heavyatom;CIC2_per_heavyatom;CIC3_per_heavyatom;CIC4;CIC5;DPSA1_per_heavyatom;DPSA2_per_heavyatom;DPSA3_per_heavyatom;DPSA4_per_heavyatom;DPSA5_per_heavyatom;DetourIndex;Diameter;ECIndex;EState_VSA1_per_heavyatom;EState_VSA10_per_heavyatom;EState_VSA2_per_heavyatom;EState_VSA3;EState_VSA4;EState_VSA5;EState_VSA6;EState_VSA7;EState_VSA8;EState_VSA9;ETA_alpha_per_heavyatom;ETA_beta_per_heavyatom;ETA_beta_ns_per_heavyatom;ETA_beta_ns_d;ETA_beta_s_per_heavyatom;ETA_dAlpha_A;ETA_dAlpha_B;ETA_dBeta_per_heavyatom;ETA_dEpsilon_A;ETA_dEpsilon_B;ETA_dEpsilon_C;ETA_dEpsilon_D;ETA_dPsi_A;ETA_dPsi_B;ETA_epsilon_1;ETA_epsilon_2;ETA_epsilon_3;ETA_epsilon_4;ETA_epsilon_5;ETA_eta_per_heavyatom;ETA_eta_B_per_heavyatom;ETA_eta_BR_per_heavyatom;ETA_eta_F_per_heavyatom;ETA_eta_FL_per_heavyatom;ETA_eta_L_per_heavyatom;ETA_eta_R_per_heavyatom;ETA_eta_RL_per_heavyatom;ETA_psi_1;ETA_shape_p;ETA_shape_x;ETA_shape_y;FCSP3;FNSA1;FNSA2_per_heavyatom;FNSA3;FNSA4;FNSA5;FPSA1;FPSA2_per_heavyatom;FPSA3;FPSA4;FPSA5;FilterItLogS;GATS1Z;GATS1are;GATS1c;GATS1d;GATS1dv;GATS1i;GATS1m;GATS1p;GATS1pe;GATS1s;GATS1se;GATS1v;GATS2Z;GATS2are;GATS2c;GATS2d;GATS2dv;GATS2i;GATS2m;GATS2p;GATS2pe;GATS2s;GATS2se;GATS2v;GATS3Z;GATS3are;GATS3c;GATS3d;GATS3dv;GATS3i;GATS3m;GATS3p;GATS3pe;GATS3s;GATS3se;GATS3v;GATS4Z;GATS4are;GATS4c;GATS4d;GATS4dv;GATS4i;GATS4m;GATS4p;GATS4pe;GATS4s;GATS4se;GATS4v;GATS5Z;GATS5are;GATS5c;GATS5d;GATS5dv;GATS5i;GATS5m;GATS5p;GATS5pe;GATS5s;GATS5se;GATS5v;GATS6Z;GATS6are;GATS6c;GATS6d;GATS6dv;GATS6i;GATS6m;GATS6p;GATS6pe;GATS6s;GATS6se;GATS6v;GATS7Z;GATS7are;GATS7c;GATS7d;GATS7dv;GATS7i;GATS7m;GATS7p;GATS7pe;GATS7s;GATS7se;GATS7v;GATS8Z;GATS8are;GATS8c;GATS8d;GATS8dv;GATS8i;GATS8m;GATS8p;GATS8pe;GATS8s;GATS8se;GATS8v;GGI1_per_heavyatom;GGI10_per_heavyatom;GGI2_per_heavyatom;GGI3_per_heavyatom;GGI4_per_heavyatom;GGI5_per_heavyatom;GGI6_per_heavyatom;GGI7_per_heavyatom;GGI8_per_heavyatom;GGI9_per_heavyatom;GRAV_per_heavyatom;GRAVH_per_heavyatom;GRAVHp_per_heavyatom;GRAVp_per_heavyatom;GeomDiameter_per_heavyatom;GeomPetitjeanIndex;GeomRadius_per_heavyatom;GeomShapeIndex;GhoseFilter;HybRatio;IC0;IC1;IC2_per_heavyatom;IC3_per_heavyatom;IC4_per_heavyatom;IC5_per_heavyatom;JGI1;JGI10;JGI2;JGI3;JGI4;JGI5;JGI6;JGI7;JGI8;JGI9;JGT10;Kier1_per_heavyatom;Kier2_per_heavyatom;Kier3_per_heavyatom;LabuteASA_per_heavyatom;Lipinski;LogEE_A_per_heavyatom;LogEE_D_per_heavyatom;LogEE_Dt;LogEE_DzZ_per_heavyatom;LogEE_Dzare_per_heavyatom;LogEE_Dzi_per_heavyatom;LogEE_Dzm_per_heavyatom;LogEE_Dzp_per_heavyatom;LogEE_Dzpe_per_heavyatom;LogEE_Dzse_per_heavyatom;LogEE_Dzv_per_heavyatom;MATS1Z;MATS1are;MATS1c;MATS1d;MATS1dv;MATS1i;MATS1m;MATS1p;MATS1pe;MATS1s;MATS1se;MATS1v;MATS2Z;MATS2are;MATS2c;MATS2d;MATS2dv;MATS2i;MATS2m;MATS2p;MATS2pe;MATS2s;MATS2se;MATS2v;MATS3Z;MATS3are;MATS3c;MATS3d;MATS3dv;MATS3i;MATS3m;MATS3p;MATS3pe;MATS3s;MATS3se;MATS3v;MATS4Z;MATS4are;MATS4c;MATS4d;MATS4dv;MATS4i;MATS4m;MATS4p;MATS4pe;MATS4s;MATS4se;MATS4v;MATS5Z;MATS5are;MATS5c;MATS5d;MATS5dv;MATS5i;MATS5m;MATS5p;MATS5pe;MATS5s;MATS5se;MATS5v;MATS6Z;MATS6are;MATS6c;MATS6d;MATS6dv;MATS6i;MATS6m;MATS6p;MATS6pe;MATS6s;MATS6se;MATS6v;MATS7Z;MATS7are;MATS7c;MATS7d;MATS7dv;MATS7i;MATS7m;MATS7p;MATS7pe;MATS7s;MATS7se;MATS7v;MATS8Z;MATS8are;MATS8c;MATS8d;MATS8dv;MATS8i;MATS8m;MATS8p;MATS8pe;MATS8s;MATS8se;MATS8v;MDEC-22;MDEC-23;MDEC-33;MIC0;MIC1;MIC2;MIC3;MIC4;MIC5;MID_per_heavyatom;MID_C_per_heavyatom;MID_N_per_heavyatom;MID_O_per_heavyatom;MID_X;MID_h_per_heavyatom;MOMI-X_per_heavyatom;MOMI-Y_per_heavyatom;MOMI-Z_per_heavyatom;MPC10;MPC2;MPC3;MPC4;MPC5;MPC6;MPC7;MPC8;MPC9;MW_per_heavyatom;MWC01;MWC02_per_heavyatom;MWC03_per_heavyatom;MWC04_per_heavyatom;MWC05_per_heavyatom;MWC06_per_heavyatom;MWC07_per_heavyatom;MWC08_per_heavyatom;MWC09_per_heavyatom;MWC10_per_heavyatom;MZ;Mare;Mi;Mm;Mor01;Mor01m;Mor01p;Mor01se;Mor01v;Mor02;Mor02m;Mor02p;Mor02se;Mor02v;Mor03;Mor03m;Mor03p;Mor03se;Mor03v;Mor04;Mor04m;Mor04p;Mor04se;Mor04v;Mor05;Mor05m;Mor05p;Mor05se;Mor05v;Mor06;Mor06m;Mor06p;Mor06se;Mor06v;Mor07;Mor07m;Mor07p;Mor07se;Mor07v;Mor08;Mor08m;Mor08p;Mor08se;Mor08v;Mor09;Mor09m;Mor09p;Mor09se;Mor09v;Mor10;Mor10m;Mor10p;Mor10se;Mor10v;Mor11;Mor11m;Mor11p;Mor11se;Mor11v;Mor12;Mor12m;Mor12p;Mor12se;Mor12v;Mor13;Mor13m;Mor13p;Mor13se;Mor13v;Mor14;Mor14m;Mor14p;Mor14se;Mor14v;Mor15;Mor15m;Mor15p;Mor15se;Mor15v;Mor16;Mor16m;Mor16p;Mor16se;Mor16v;Mor17;Mor17m;Mor17p;Mor17se;Mor17v;Mor18;Mor18m;Mor18p;Mor18se;Mor18v;Mor19;Mor19m;Mor19p;Mor19se;Mor19v;Mor20;Mor20m;Mor20p;Mor20se;Mor20v;Mor21;Mor21m;Mor21p;Mor21se;Mor21v;Mor22;Mor22m;Mor22p;Mor22se;Mor22v;Mor23;Mor23m;Mor23p;Mor23se;Mor23v;Mor24;Mor24m;Mor24p;Mor24se;Mor24v;Mor25;Mor25m;Mor25p;Mor25se;Mor25v;Mor26;Mor26m;Mor26p;Mor26se;Mor26v;Mor27;Mor27m;Mor27p;Mor27se;Mor27v;Mor28;Mor28m;Mor28p;Mor28se;Mor28v;Mor29;Mor29m;Mor29p;Mor29se;Mor29v;Mor30;Mor30m;Mor30p;Mor30se;Mor30v;Mor31;Mor31m;Mor31p;Mor31se;Mor31v;Mor32;Mor32m;Mor32p;Mor32se;Mor32v;Mp;Mpe;Mse;Mv;NaaCH;NaaN;NaaNH;NaaO;NaaS;NaaaC;NaasC;NaasN;NdCH2;NdO;NdS;NddsN;NddssS;NdsCH;NdsN;NdssC;NsBr;NsCH3;NsCl;NsF;NsNH2;NsOH;NssCH2;NssNH;NssO;NssS;NsssCH;NsssN;NssssC;NtN;NtsC;PEOE_VSA1_per_heavyatom;PEOE_VSA10_per_heavyatom;PEOE_VSA11;PEOE_VSA12_per_heavyatom;PEOE_VSA13;PEOE_VSA2_per_heavyatom;PEOE_VSA3;PEOE_VSA4;PEOE_VSA5;PEOE_VSA6_per_heavyatom;PEOE_VSA7_per_heavyatom;PEOE_VSA8_per_heavyatom;PEOE_VSA9;PNSA1_per_heavyatom;PNSA2_per_heavyatom;PNSA3_per_heavyatom;PNSA4_per_heavyatom;PNSA5_per_heavyatom;PPSA1_per_heavyatom;PPSA2_per_heavyatom;PPSA3_per_heavyatom;PPSA4_per_heavyatom;PPSA5_per_heavyatom;PetitjeanIndex;RASA;RNCG_per_heavyatom;RNCS_per_heavyatom;RPCG_per_heavyatom;RPCS;RPSA;Radius;RotRatio;SIC0_per_heavyatom;SIC1_per_heavyatom;SIC2;SIC3;SIC4;SIC5;SLogP;SM1_Dt;SM1_DzZ_per_heavyatom;SM1_Dzare_per_heavyatom;SM1_Dzi_per_heavyatom;SM1_Dzm_per_heavyatom;SM1_Dzp_per_heavyatom;SM1_Dzpe_per_heavyatom;SM1_Dzse_per_heavyatom;SM1_Dzv_per_heavyatom;SMR_per_heavyatom;SMR_VSA1_per_heavyatom;SMR_VSA2;SMR_VSA3_per_heavyatom;SMR_VSA4;SMR_VSA5_per_heavyatom;SMR_VSA6;SMR_VSA7;SMR_VSA8;SMR_VSA9;SRW02_per_heavyatom;SRW03;SRW04_per_heavyatom;SRW05;SRW06_per_heavyatom;SRW07;SRW08_per_heavyatom;SRW09;SRW10_per_heavyatom;SZ_per_heavyatom;SaaCH;SaaN;SaaNH;SaaO;SaaS;SaaaC;SaasC;SaasN;Sare_per_heavyatom;SdCH2;SdO_per_heavyatom;SdS;SddsN;SddssS;SdsCH;SdsN;SdssC_per_heavyatom;Si_per_heavyatom;SlogP_VSA1_per_heavyatom;SlogP_VSA10;SlogP_VSA11;SlogP_VSA2_per_heavyatom;SlogP_VSA3_per_heavyatom;SlogP_VSA4;SlogP_VSA5_per_heavyatom;SlogP_VSA6;SlogP_VSA7;SlogP_VSA8;SlogP_VSA9;Sm_per_heavyatom;Sp_per_heavyatom;SpAD_A_per_heavyatom;SpAD_D_per_heavyatom;SpAD_Dt;SpAD_DzZ_per_heavyatom;SpAD_Dzare_per_heavyatom;SpAD_Dzi_per_heavyatom;SpAD_Dzm_per_heavyatom;SpAD_Dzp_per_heavyatom;SpAD_Dzpe_per_heavyatom;SpAD_Dzse_per_heavyatom;SpAD_Dzv_per_heavyatom;SpAbs_A_per_heavyatom;SpAbs_D_per_heavyatom;SpAbs_Dt;SpAbs_DzZ_per_heavyatom;SpAbs_Dzare_per_heavyatom;SpAbs_Dzi_per_heavyatom;SpAbs_Dzm_per_heavyatom;SpAbs_Dzp_per_heavyatom;SpAbs_Dzpe_per_heavyatom;SpAbs_Dzse_per_heavyatom;SpAbs_Dzv_per_heavyatom;SpDiam_A;SpDiam_D_per_heavyatom;SpDiam_Dt;SpDiam_DzZ_per_heavyatom;SpDiam_Dzare_per_heavyatom;SpDiam_Dzi_per_heavyatom;SpDiam_Dzm_per_heavyatom;SpDiam_Dzp_per_heavyatom;SpDiam_Dzpe_per_heavyatom;SpDiam_Dzse_per_heavyatom;SpDiam_Dzv_per_heavyatom;SpMAD_A;SpMAD_D_per_heavyatom;SpMAD_Dt;SpMAD_DzZ_per_heavyatom;SpMAD_Dzare_per_heavyatom;SpMAD_Dzi_per_heavyatom;SpMAD_Dzm_per_heavyatom;SpMAD_Dzp_per_heavyatom;SpMAD_Dzpe_per_heavyatom;SpMAD_Dzse_per_heavyatom;SpMAD_Dzv_per_heavyatom;SpMax_A;SpMax_D_per_heavyatom;SpMax_Dt;SpMax_DzZ_per_heavyatom;SpMax_Dzare_per_heavyatom;SpMax_Dzi_per_heavyatom;SpMax_Dzm_per_heavyatom;SpMax_Dzp_per_heavyatom;SpMax_Dzpe_per_heavyatom;SpMax_Dzse_per_heavyatom;SpMax_Dzv_per_heavyatom;Spe_per_heavyatom;SsBr;SsCH3;SsCl;SsF;SsNH2;SsOH;Sse_per_heavyatom;SssCH2;SssNH_per_heavyatom;SssO;SssS;SsssCH_per_heavyatom;SsssN;SssssC;StN;StsC;Sv_per_heavyatom;TASA_per_heavyatom;TIC0_per_heavyatom;TIC1_per_heavyatom;TIC2_per_heavyatom;TIC3_per_heavyatom;TIC4_per_heavyatom;TIC5_per_heavyatom;TMPC10;TMWC10_per_heavyatom;TPSA_per_heavyatom;TSRW10_per_heavyatom;TopoPSA_per_heavyatom;TopoPSA(NO)_per_heavyatom;TopoShapeIndex;TpiPC10_per_heavyatom;VAdjMat_per_heavyatom;VE1_A_per_heavyatom;VE1_D_per_heavyatom;VE1_Dt;VE1_DzZ_per_heavyatom;VE1_Dzare_per_heavyatom;VE1_Dzi_per_heavyatom;VE1_Dzm_per_heavyatom;VE1_Dzp_per_heavyatom;VE1_Dzpe_per_heavyatom;VE1_Dzse_per_heavyatom;VE1_Dzv_per_heavyatom;VE2_A_per_heavyatom;VE2_D_per_heavyatom;VE2_Dt;VE2_DzZ_per_heavyatom;VE2_Dzare_per_heavyatom;VE2_Dzi_per_heavyatom;VE2_Dzm_per_heavyatom;VE2_Dzp_per_heavyatom;VE2_Dzpe_per_heavyatom;VE2_Dzse_per_heavyatom;VE2_Dzv_per_heavyatom;VE3_A_per_heavyatom;VE3_D_per_heavyatom;VE3_Dt;VE3_DzZ_per_heavyatom;VE3_Dzare_per_heavyatom;VE3_Dzi_per_heavyatom;VE3_Dzm_per_heavyatom;VE3_Dzp_per_heavyatom;VE3_Dzpe_per_heavyatom;VE3_Dzse_per_heavyatom;VE3_Dzv_per_heavyatom;VMcGowan_per_heavyatom;VR1_A_log_scaled;VR1_D_per_heavyatom;VR1_Dt;VR1_DzZ_per_heavyatom;VR1_Dzare_per_heavyatom;VR1_Dzi_per_heavyatom;VR1_Dzm_per_heavyatom;VR1_Dzp_per_heavyatom;VR1_Dzpe_per_heavyatom;VR1_Dzse_per_heavyatom;VR1_Dzv_per_heavyatom;VR2_A_log_scaled;VR2_D_per_heavyatom;VR2_Dt;VR2_DzZ_per_heavyatom;VR2_Dzare_per_heavyatom;VR2_Dzi_per_heavyatom;VR2_Dzm_per_heavyatom;VR2_Dzp_per_heavyatom;VR2_Dzpe_per_heavyatom;VR2_Dzse_per_heavyatom;VR2_Dzv_per_heavyatom;VR3_A_per_heavyatom;VR3_D_per_heavyatom;VR3_Dt;VR3_DzZ_per_heavyatom;VR3_Dzare_per_heavyatom;VR3_Dzi_per_heavyatom;VR3_Dzm_per_heavyatom;VR3_Dzp_per_heavyatom;VR3_Dzpe_per_heavyatom;VR3_Dzse_per_heavyatom;VR3_Dzv_per_heavyatom;VSA_EState1_per_heavyatom;VSA_EState2_per_heavyatom;VSA_EState3_per_heavyatom;VSA_EState4_per_heavyatom;VSA_EState5_per_heavyatom;VSA_EState6_per_heavyatom;VSA_EState7_per_heavyatom;VSA_E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diff --git a/atomsci/ddm/data/moe_svl/custom/ksm_svl/smp_WashMinimizeSMILES.svl b/atomsci/ddm/data/moe_svl/custom/ksm_svl/smp_WashMinimizeSMILES.svl
new file mode 100644
index 00000000..a3f09839
--- /dev/null
+++ b/atomsci/ddm/data/moe_svl/custom/ksm_svl/smp_WashMinimizeSMILES.svl
@@ -0,0 +1,196 @@
+#svl
+//	smp_WashMinimizeSMILES.svl	Washing and minimizing a SMILES
+//
+//	21-mar-2019 (gk) created
+//
+// COPYRIGHT (C) 2019 CHEMICAL COMPUTING GROUP ULC.  ALL RIGHTS RESERVED.
+//
+// PERMISSION TO USE, COPY, MODIFY AND DISTRIBUTE THIS SOFTWARE IS HEREBY
+// GRANTED PROVIDED THAT: (1) UNMODIFIED OR FUNCTIONALLY EQUIVALENT CODE
+// DERIVED FROM THIS SOFTWARE MUST CONTAIN THIS NOTICE; (2) ALL CODE DERIVED
+// FROM THIS SOFTWARE MUST ACKNOWLEDGE THE AUTHOR(S) AND INSTITUTION(S); (3)
+// THE NAMES OF THE AUTHOR(S) AND INSTITUTION(S) NOT BE USED IN ADVERTISING
+// OR PUBLICITY PERTAINING TO THE DISTRIBUTION OF THE SOFTWARE WITHOUT
+// SPECIFIC, WRITTEN PRIOR PERMISSION; (4) ALL CODE DERIVED FROM THIS SOFTWARE
+// BE EXECUTED WITH THE MOLECULAR OPERATING ENVIRONMENT (MOE) LICENSED FROM
+// CHEMICAL COMPUTING GROUP ULC.
+//
+// CHEMICAL COMPUTING GROUP ULC. DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS
+// SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS,
+// AND IN NO EVENT SHALL CHEMICAL COMPUTING GROUP ULC. BE LIABLE FOR ANY
+// SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER
+// RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF
+// CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN
+// CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
+//
+//
+//	DESCRIPTION
+//	MOE/smp version of ph4_Wash, Minimze and sm_Build
+//
+//	USAGE:  invoke MOE/smp
+//		run ['smp_WashMinimizeSMILES.svl', 'database', 'smilesfield']
+//	       
+//
+
+#set title 	'smp_WashMinimizeSMILES'
+#set class 	'MOE:SMP'
+#set main	'smp_main'
+#set version 	'2019.03'
+
+//------------------------------------------------------------------------------
+// imports
+//------------------------------------------------------------------------------
+
+function ph4_Wash, MM;
+
+//-----------------------------------------------------------------------------------
+// constants
+//-----------------------------------------------------------------------------------
+
+//const SCRIPT_FILENAME = 'smp_WashMinimizeSMILES.svl';
+const GLOBAL_FUNCTION = 'smp_WashMinimizeSMILES';
+const OUT_FIELDNAME   = 'mol_prep';
+const OUTFIELDTYPE    = 'molecule';
+
+//-----------------------------------------------------------------------------------
+// global functions
+//-----------------------------------------------------------------------------------
+
+global function smp_WashMinimizeSMILES smi
+
+    local mol, chain, atoms;
+
+    if sm_BuildSyntax smi === 0 then
+	return [];
+    endif
+
+    // create molecule
+    atoms = sm_Build smi;
+    chain = uniq aChain atoms;
+
+    // wash
+    local washed_atoms = first ph4_Wash[
+	atoms, 
+	[
+	    one_component: 1, delete_atoms: 1, coord_scale: 1, 
+	    set_fc: 1, acid_base: 1, add_h: 1
+        ]
+    ];
+   
+    // minimize
+    pot_FinalizeModel[];
+    (MM[]);
+
+    // get db_mol vector of updated molecule
+    mol = mol_Extract washed_atoms;
+
+    // destroy molecule from system
+    oDestroy chain;
+
+    // return db_mol vector
+    return mol;
+endfunction
+
+//-----------------------------------------------------------------------------------
+// local functions
+//-----------------------------------------------------------------------------------
+
+local function db_function_call [dbname, smfield]
+
+    // local variables
+    local mdb_key; 
+    local cmd, entry, task_id;
+    local src, i, smi;
+    local udataS, udataR, res, code, seq;
+    local SCRIPT_FILENAME = (modenv[]).filename;
+
+    // close system at start
+    Close [force: 1, viewreset: 1, delgobj: 1];
+
+    // open database and find fields of type molecule
+    mdb_key = db_Open [dbname, 'read-write'];
+    
+    // create fields for output
+    db_EnsureField [mdb_key, OUT_FIELDNAME, OUTFIELDTYPE];
+    db_EnsureField [mdb_key, 'smp_seq', 'int'];
+
+    // initialize variable for loop
+    cmd = 'call';
+    entry = 0;
+    task_id = task_key -1;
+
+    // distribute script to all nodes
+    src = freadb [SCRIPT_FILENAME, 'char', INT_MAX];
+    mpu_load [igen MPU_HOSTCOUNT, [src]];
+
+    // loop through database
+    for i = 1, INT_MAX loop
+
+	// get next molecules
+	if anytrue cmd then
+	    entry = db_NextEntry [mdb_key, entry];
+	    smi = token first db_ReadFields [mdb_key, entry, smfield];
+	endif
+
+	// set command to '' for collecting the last results
+	if allfalse entry then cmd = ''; endif
+
+	// MOE/smp call of global function
+	udataS = [task_id, entry];
+	[res, code, seq, udataR] = mpu_batch [cmd, GLOBAL_FUNCTION, smi, udataS];
+
+	// check return code
+	if code === 'skip' then
+
+	    // do nothing (skip is returned for the first calls when no results are to
+	    // be transferred back to the master
+
+	elseif code === 'error' then
+
+	    // we are in trouble !
+	    exit twrite['{}', res];
+
+	elseif code === 'eof' then
+
+	    // end is reached
+	    break;
+
+	elseif udataR(1) === task_id then
+
+	    // write updated molecule to database
+	    db_Write [mdb_key, udataR(2),tag[[OUT_FIELDNAME, 'smp_seq'], [res, i]]];
+
+	else
+
+	    // we are in trouble
+	    exit '!!!';
+
+	endif
+    endloop
+
+    // close database
+    db_Close mdb_key;
+
+    // inform user that run is complete
+    pr "finished";
+
+endfunction
+
+
+//====================================================================================
+// main function
+//====================================================================================
+
+local function smp_main [database, smilesfield]
+
+    local SCRIPT_FILENAME = (modenv[]).filename;
+    // check is argument is correct
+    if isnull database then 
+	pr cat["Usage: run ['", string SCRIPT_FILENAME, "', ['databasefilename', 'smilesfield']];"];
+    else
+	sleep 1;
+
+	// call smp function on database
+	db_function_call [database, smilesfield];
+    endif
+endfunction
diff --git a/atomsci/ddm/data/moe_svl/custom/svl/db_desc_smp5.svl b/atomsci/ddm/data/moe_svl/custom/svl/db_desc_smp5.svl
new file mode 100644
index 00000000..9e56b265
--- /dev/null
+++ b/atomsci/ddm/data/moe_svl/custom/svl/db_desc_smp5.svl
@@ -0,0 +1,356 @@
+#svl
+//	db_desc_smp.svl	Calculate descriptors for a database with MOE/smp
+//
+//	26-feb-2019 (gk) renamed mol_desc
+//	15-mar-2018 (me) fixed typo in validation if value already calculated
+//	05-mar-2018 (me) added calculate_all option to skip over calculated values
+//	13-nov-2017 (BB) print entry number
+//	01-mar-2011 (ah) bug fix for *acalc* descriptors
+//	28-feb-2011 (ah) replicate molecules from main MOE window to other nodes
+//	25-feb-2011 (sg) change default options (do nothing == default)
+//	25-feb-2011 (sg) make the SVL find itself
+//	25-oct-2010 (ah) created, based on from smp_template.svl
+//	20-jan-2005 (gk) added set priority = 1 on nodes
+//	17-dec-2004 (gk) rewritten as template
+//	23-nov-2004 (ms) created
+//
+// COPYRIGHT (C) 2019 CHEMICAL COMPUTING GROUP ULC ("CCG").
+// ALL RIGHTS RESERVED.
+//
+// PERMISSION TO USE, COPY, MODIFY AND DISTRIBUTE THIS SOFTWARE IS HEREBY
+// GRANTED PROVIDED THAT: (1) UNMODIFIED OR FUNCTIONALLY EQUIVALENT SOFTWARE
+// DERIVED FROM THIS SOFTWARE MUST CONTAIN THIS NOTICE; (2) ALL CODE DERIVED
+// FROM THIS SOFTWARE MUST ACKNOWLEDGE THE AUTHOR(S) AND INSTITUTION(S); (3)
+// THE NAMES OF THE AUTHOR(S) AND INSTITUTION(S) NOT BE USED IN ADVERTISING
+// OR PUBLICITY PERTAINING TO THIS SOFTWARE WITHOUT SPECIFIC WRITTEN PRIOR
+// PERMISSION; (4) ALL CODE DERIVED FROM THIS SOFTWARE BE EXECUTED WITH THE
+// MOLECULAR OPERATING ENVIRONMENT LICENSED FROM CCG.
+//
+// CCG DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE, INCLUDING
+// ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS, AND IN NO EVENT
+// SHALL CCG BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR
+// ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER
+// IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT
+// OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
+
+//
+//
+//	DESCRIPTION
+//
+//	Calculate descriptors for a MDB database of compounds, using MOE/smp
+//	If a molecule field is not specified, the field closest to the left
+//	edge of the database will be used.
+//	
+//	If no descriptor codes are specified, by default no descriptors will be
+//	calculated.
+//
+//	USAGE:  invoke MOE/smp and then
+//		run ['db_desc_smp.svl', [mdbfile, mfield, codes, opt]]
+//
+//	calculate all descriptors except the ones from MOPAC:
+//		run ['db_desc_smp.svl', 'lig.mdb']
+//
+//	calculate 32 VSA binned descriptors as well as Weight and TPSA
+//  run ['db_desc_smp.svl',['lig.mdb',[],['Weight','TPSA'],[codeset:'VSA_32']]]
+//
+//      calculate Lipinski and Oprea descriptors and no others for washed field
+//  run['db_desc_smp.svl',['lig.mdb','washed',['lip*','opr*'],[codeset:'None']]]
+//
+//	Run from MOE/batch with 3 CPUs or cores
+//
+//     $MOE/bin/moebatch -mpu 3 -exec "run ['db_desc_smp.svl', 'lig.mdb']" -exit
+//
+
+#set title 	'MOE/smp descriptor calculation'
+#set main	'db_desc_smp'
+#set version 	'2010.10'
+
+function QuaSAR_Descriptor_List, PartialChargeMethods, PartialChargeMDB;
+function __FlexAlign_Replicate;
+
+const DEFAULTS = [
+    codeset: 'None',	// 'All_No_MOPAC': all non-MOPAC descriptors
+    				// 'MOPAC'       : all MOPAC descriptors
+    				// '2D'          : all 2D descriptors
+    				// '3D'          : all 3D descriptors
+    				// 'x3D'         : all x3D descriptors
+    				// 'i3D'         : all i3D descriptors
+    				// 'i3DNotMOPAC' : all i3D descriptors except MOPAC
+    				// 'VSA_32'      : 32 binned property VSA descriptors
+    				// 'All'         : all descriptors
+				// 'None'	 : only specified descriptors
+    forcefield:	'',		// filename of forcefield to use
+    charge_meth: '',		// forcefield partial charges for fragment
+				// eg '' to leave unchanged, 'FF' for forcefield
+				// or 'AM1-BCC' for AM1-BCC charges
+    add_h: 1,			// add hydrogens
+    calculate_all: 1		// calculate all descriptors or just missing
+];
+
+//const SCRIPT_FILENAME = 'db_desc_smp.svl';	// replaced with local
+const GLOBAL_FUNCTION = 'smp_DescriptorCalcMol';
+const OUT_FIELDNAME = 'smp_seq';
+const i3DNotMOPAC = [
+'ASA', 'ASA+', 'ASA-', 'ASA_H', 'ASA_P', 'CASA+', 'CASA-', 'DASA', 'DCASA',
+'dens', 'dipole', 'dipoleX', 'dipoleY', 'dipoleZ', 'E', 'E_ang', 'E_ele',
+'E_nb', 'E_oop', 'E_rele', 'E_rnb', 'E_rsol', 'E_rvdw', 'E_sol', 'E_stb', 
+'E_str', 'E_strain', 'E_tor', 'E_vdw', 'FASA+', 'FASA-', 'FASA_H', 'FASA_P',
+'FCASA+', 'FCASA-', 'glob', 'npr1', 'npr2', 'pmi', 'pmi1', 'pmi2', 'pmi3', 
+'pmiX', 'pmiY', 'pmiZ', 'rgyr', 'std_dim1', 'std_dim2', 'std_dim3', 'vol', 
+'VSA', 'vsurf_A', 'vsurf_CP', 'vsurf_CW1', 'vsurf_CW2', 'vsurf_CW3', 'vsurf_CW4',
+'vsurf_CW5', 'vsurf_CW6', 'vsurf_CW7', 'vsurf_CW8', 'vsurf_D1', 'vsurf_D2', 'vsurf_D3', 
+'vsurf_D4', 'vsurf_D5', 'vsurf_D6', 'vsurf_D7', 'vsurf_D8', 'vsurf_DD12', 'vsurf_DD13',
+'vsurf_DD23', 'vsurf_DW12', 'vsurf_DW13', 'vsurf_DW23', 'vsurf_EDmin1', 'vsurf_EDmin2',
+'vsurf_EDmin3', 'vsurf_EWmin1', 'vsurf_EWmin2', 'vsurf_EWmin3', 'vsurf_G', 'vsurf_HB1', 
+'vsurf_HB2', 'vsurf_HB3', 'vsurf_HB4', 'vsurf_HB5', 'vsurf_HB6', 'vsurf_HB7', 'vsurf_HB8', 
+'vsurf_HL1', 'vsurf_HL2', 'vsurf_ID1', 'vsurf_ID2', 'vsurf_ID3', 'vsurf_ID4', 'vsurf_ID5', 
+'vsurf_ID6', 'vsurf_ID7', 'vsurf_ID8', 'vsurf_IW1', 'vsurf_IW2', 'vsurf_IW3', 'vsurf_IW4', 
+'vsurf_IW5', 'vsurf_IW6', 'vsurf_IW7', 'vsurf_IW8', 'vsurf_R', 'vsurf_S', 'vsurf_V', 'vsurf_W1', 
+'vsurf_W2', 'vsurf_W3', 'vsurf_W4', 'vsurf_W5', 'vsurf_W6', 'vsurf_W7', 'vsurf_W8', 'vsurf_Wp1', 
+'vsurf_Wp2', 'vsurf_Wp3', 'vsurf_Wp4', 'vsurf_Wp5', 'vsurf_Wp6', 'vsurf_Wp7', 'vsurf_Wp8'
+]; //BB
+
+const VSA_32 = [
+    'PEOE_VSA-6', 'PEOE_VSA-5', 'PEOE_VSA-4', 'PEOE_VSA-3', 'PEOE_VSA-2',
+    'PEOE_VSA-1', 'PEOE_VSA-0', 'PEOE_VSA+0', 'PEOE_VSA+1', 'PEOE_VSA+2',
+    'PEOE_VSA+3', 'PEOE_VSA+4', 'PEOE_VSA+5', 'PEOE_VSA+6',
+ 
+    'SlogP_VSA0', 'SlogP_VSA1', 'SlogP_VSA2', 'SlogP_VSA3', 'SlogP_VSA4',
+    'SlogP_VSA5', 'SlogP_VSA6', 'SlogP_VSA7', 'SlogP_VSA8', 'SlogP_VSA9',
+ 
+    'SMR_VSA0'  , 'SMR_VSA1'  , 'SMR_VSA2'  , 'SMR_VSA3'  , 'SMR_VSA4'  ,
+    'SMR_VSA5'  , 'SMR_VSA6'  , 'SMR_VSA7'
+];
+
+global function smp_DescriptorCalcMol [mol, descriptors]
+    local i, j = 0, codes, fcns, acalc, desc, counts, splcodes, splfcns;
+    local splacalc, chains, atoms;
+
+    [codes, fcns, acalc] = descriptors;
+
+    counts = mtoc m_uniq fcns;
+    [splcodes, splfcns, splacalc] = apt split [[codes, fcns, acalc], [counts]];
+
+    if anytrue acalc then 
+	chains = mol_Create mol;
+	atoms = cat cAtoms chains;
+    endif
+
+    for i = 1, length counts loop
+	j = inc j;
+	if istrue splacalc(i)(1) then
+	    desc(j) = call [splfcns(i)(1), [atoms, splcodes(i)]];
+	endif
+    endloop
+
+    if anytrue acalc then oDestroy chains;endif
+
+    for i = 1, length counts loop
+	j = inc j;
+	if isfalse splacalc(i)(1) then
+	    desc(j) = call [splfcns(i)(1), [  mol, splcodes(i)]];
+	endif
+    endloop
+
+    return cat desc;
+endfunction
+
+
+local function desc_smp [mdbfile, mfield, codes, opt]
+
+    local SCRIPT_FILENAME = (modenv[]).filename;
+    local mdb, cmd = 'call', ent=0, task_id, src, i, mol, udataS, record; 
+    local udataR, res, code, seq; 
+
+    mdb = db_Open mdbfile;
+    
+    local entno, entries = db_Entries mdb;    //(nl)
+
+	// create fields for output
+    db_EnsureField [mdb, OUT_FIELDNAME, 'int'];
+    apt db_EnsureField [mdb, codes, 'float'];
+
+    // initialize variable for loop
+    task_id = task_key -1;
+
+    local descinfo = QuaSAR_Descriptor_List [];
+
+	//ignore unrecognised descriptor codes
+    local descmask = indexof [codes, descinfo(1)];
+    if anyfalse descmask then
+	write 'Unrecognised descriptor codes:\n';
+	apt write ['{}\n', codes | not descmask];
+	write '\n';
+	codes = codes | descmask;
+    endif
+
+    local fcns = get [descinfo(5), pack descmask];
+    local acalc= get [descinfo(6), pack descmask]; 
+    [codes, fcns, acalc] = apt get [[codes, fcns, acalc],[x_sort fcns]];
+    local descs = [codes, fcns, acalc];
+
+	// Copy molecules from the main MOE window to all the MOE/smp nodes
+
+    __FlexAlign_Replicate [];			// replicate to MOE/smp
+
+	// distribute script to all nodes
+    src = freadb [SCRIPT_FILENAME, 'char', INT_MAX];
+    mpu_load [igen MPU_HOSTCOUNT, [src]];
+
+    for i = 1, INT_MAX loop
+	    // get next molecules
+	if anytrue cmd then
+	    ent = db_NextEntry [mdb, ent];
+	    record = db_Read [mdb, ent];
+            mol = first db_ReadFields [mdb, ent, mfield]; // (ME) fix 
+	   // mol = record.mol;  
+	endif
+
+	    // set command to '' for collecting the last results
+	    // or break if end of db and still call (nothing to do)
+	if ent == 0 and isnull code then break;
+	elseif ent == 0 then cmd = ''; endif
+
+	    // check if something to do for this entry
+	    // if calculate_all is 1 all descriptors will be calculated
+	    // if calculate_all is 0 only descriptos of empyt fields will be calculated
+	local record_descs;
+	if opt.calculate_all then
+	    record_descs = descs;
+	else
+	    record_descs = apt mget [descs, [app isnull tagget [record, codes]]];
+	    if allfalse record_descs and anytrue record then
+		continue;
+	    endif
+	endif
+
+	    // MOE/smp call of global function, keep track of the desc calculate for this entry
+	udataS = [task_id, ent, record_descs(1)];
+	[res,code,seq,udataR]=mpu_batch[cmd,GLOBAL_FUNCTION,[mol,record_descs],udataS];
+
+	    // check return code
+	if code == 'skip' then
+	    // do nothing (skip is returned for the first calls when no results
+	    // are to be transferred back to the master
+
+	elseif code == 'error' then // we are in trouble !
+	    exit res;
+
+	elseif code == 'eof' then // end is reached
+	    break;
+
+	elseif udataR(1) === task_id then
+	    // only write calculated fields to mdb
+	    local data = tag [udataR(3), res];
+	    data.(OUT_FIELDNAME) = seq; 
+	    db_Write [mdb, udataR(2), data];
+	    entno = indexof[udataR(2), entries];    // BB
+
+	else // uh oh, we are in trouble
+	    exit 'Error';
+	endif
+    endloop
+    sleep 1;
+    db_Close mdb;
+endfunction
+
+local function get_descriptors [codes, codeset]
+
+    local [allcodes, descriptions, classes] = QuaSAR_Descriptor_List [];
+    local m_mopac = m_findmatch [['PM3_*', 'AM1_*', 'MNDO_*'], allcodes];
+    local m_2d  = classes ==  '2D';
+    local m_x3d = classes == 'x3D';
+    local m_i3d = classes == 'i3D';
+    local m_pro = classes == 'Protein';
+
+    local m_codes = apt m_findmatch [codes, [allcodes]];
+    if anytrue app allfalse m_codes then
+	write ['Ignoring unrecognised descriptor{}:\n', 
+	    select ['s', '', length pack app allfalse m_codes > 1]];
+	apt write ['    {}\n', codes | app allfalse m_codes];
+	write '\n';
+    endif
+
+    codes = allcodes | orE m_codes;
+
+    if codeset === 'MOPAC' then
+	allcodes = allcodes | m_mopac;
+    elseif codeset === '2D' then
+	allcodes = allcodes | m_2d;
+    elseif codeset === '3D' then
+	allcodes = allcodes | m_x3d or m_i3d;
+    elseif codeset === 'x3D' then
+	allcodes = allcodes | m_x3d;
+    elseif codeset === 'i3D' then
+	allcodes = allcodes | m_i3d;
+    elseif codeset === 'All_No_MOPAC' then
+	allcodes = allcodes | not m_mopac;
+    elseif codeset === 'All_No_MOPAC_Protein' then   //BB
+	allcodes = allcodes | not orE [m_mopac, m_pro];
+    elseif codeset === 'VSA_32' then
+	allcodes = join [VSA_32, allcodes];
+    elseif codeset === 'i3DNotMOPAC' then
+	allcodes = join [i3DNotMOPAC, allcodes];
+    elseif codeset === 'All' then
+	// do nothing
+    elseif codeset === 'None' then
+	allcodes = [];
+    endif
+
+
+    return uniq cat [allcodes, codes];
+endfunction
+
+
+//=====================================================================
+// main function
+//=====================================================================
+
+global function db_desc_smp [mdbfile, mfield, codes, opt]
+
+    local mdb;
+
+    if MOE_BATCH and isnull mdbfile then 
+	exit 'Usage: run [\'db_desc_smp3.svl\', [mdbfile, mfield, codes, opt]]';
+    endif
+
+    if istrue indexof [storage mdbfile, ['tok', 'int']] then
+	mdb = db_Open [mdbfile, 'read-write'];
+    else 
+	mdb = dbv_DefaultView [];
+	mdbfile = db_Filename mdb;
+    endif
+
+    if isnull mfield then
+	mfield = db_FirstFieldType [mdb, 'molecule'];
+    endif
+
+    opt = tagcat [opt, DEFAULTS];
+
+    if tok_length opt.forcefield then
+	if ftype opt.forcefield == 'file' then pot_Load opt.forcefield;endif
+    endif
+
+    if istrue tok_length opt.charge_meth then
+	if not indexof [opt.charge_meth, first PartialChargeMethods []] then
+	    exit twrite ['{}: invalid partial charge method',
+		opt.charge_meth
+	    ];
+	else
+	    PartialChargeMDB [mdbfile, opt.charge_meth, mfield, mfield,
+		[add_h: opt.add_h]
+	    ];
+	endif
+    endif
+
+    sleep 1;
+
+	// set priority on all but parent nodes to highest
+    if MPU_HOSTCOUNT > 1 then
+	mpu_call [inc igen dec MPU_HOSTCOUNT, 'exe_setpriority', [[0,1]]];
+    endif
+
+	// call smp function on database
+    desc_smp [mdbfile, mfield, get_descriptors [codes, opt.codeset], opt];
+
+endfunction
diff --git a/atomsci/ddm/docs/PARAMETERS.md b/atomsci/ddm/docs/PARAMETERS.md
index aa98e952..a0660a80 100644
--- a/atomsci/ddm/docs/PARAMETERS.md
+++ b/atomsci/ddm/docs/PARAMETERS.md
@@ -16,7 +16,6 @@ The AMPL pipeline contains many parameters and options to fit models and make pr
   - [Hybrid model](#Hybrid-model)
   - [Splitting](#Splitting)
   - [Transformers](#Transformers)
-  - [UMAP](#UMAP)
   - [XGBoost](#XGBoost)
   - [Additional DeepChem Models](#Auto-DCModels)
 - [Model Saving](#Model-Saving)
@@ -626,16 +625,17 @@ the model will train for max_epochs regardless of validation error.|
   
 |||
 |-|-|
-|*Description:*|type of transformation for the features|
-|*Default:*|normalization|
-|*Type:*|Choice|
+|*Description:*|type of transformation for the features. Choices are {"normalization", "RobustScaler", "PowerTransformer", "Identity"}.|
+|*Default:*|"normalizaton"|
+|*Type:*|choice|
   
 - **response\_transform\_type**  
   
 |||
 |-|-|
-|*Description:*|type of transformation for the response column (defaults to "normalization") TODO: Not currently implemented|
-|*Default:*|normalization|
+|*Description:*|type of transformation for the response column. Choices are {"normalization"}|
+|*Default:*|"normalization"|
+|*Type:*|choice|
   
 - **weight\_transform\_type**  
   
@@ -673,46 +673,63 @@ the model will train for max_epochs regardless of validation error.|
 |*Default:*|TRUE|
 |*Type:*|Bool|
   
----
-
-<a name="UMAP"></a>
-## UMAP  
-
-- **umap\_dim**  
+- **robustscaler_with_centering**  
   
 |||
 |-|-|
-|*Description:*|Dimension of projected feature space, if UMAP transformation is requested. Can be input as a comma separated list for hyperparameter search (e.g. '2,6,10').|
-|*Default:*|10|
-  
-- **umap\_metric**  
+|*Description:*|If `True`, center the data before scaling. This will cause `transform` to raise an exception when attempted on sparse matrices, because centering them entails building a dense matrix which in common use cases is likely to be too large to fit in memory.|
+|*Default:*|TRUE|
+|*Type:*|Bool|
+
+- **robustscaler_with_scaling**  
   
 |||
 |-|-|
-|*Description:*|Distance metric used, if UMAP transformation is requested. Can be input as a comma separated list for hyperparameter search (e.g. 'euclidean','cityblock')|
-|*Default:*|euclidean|
-  
-- **umap\_min\_dist**  
+|*Description:*|If `True`, scale the data to interquartile range.|
+|*Default:*|TRUE|
+|*Type:*|Bool|
+
+  - **robustscaler_quartile_range**  
   
 |||
 |-|-|
-|*Description:*|Minimum distance used in UMAP projection, if UMAP transformation is requested. Can be input as a comma separated list for hyperparameter search (e.g. '0.01,0.02,0.05')|
-|*Default:*|0.05|
-  
-- **umap\_neighbors**  
+|*Description:*|Quantile range used to calculate `scale_`. By default this is equal to the IQR, i.e., `q_min` is the first quantile and `q_max` is the third quantile. `(q_min, q_max), 0.0 < q_min < q_max < 100.0`|
+|*Default:*|(25.0, 75.0)|
+|*Type:*|List|
+
+  - **robustscaler_unit_variance**  
   
 |||
 |-|-|
-|*Description:*|Number of nearest neighbors used in UMAP projection, if UMAP transformation is requested. Can be input as a comma separated list for hyperparameter search (e.g. '10,20,30')|
-|*Default:*|20|
+|*Description:*|If `True`, scale data so that normally distributed features have a variance of 1. In general, if the difference between the x-values of `q_max` and `q_min` for a standard normal distribution is greater than 1, the dataset will be scaled down. If less than 1, the dataset will be scaled up.|
+|*Default:*|FALSE|
+|*Type:*|bool|
+
+  - **powertransformer_method**  
   
-- **umap\_targ\_wt**  
+|||
+|-|-|
+|*Description:*|The power transform method. Available methods are: ‘yeo-johnson’ , works with positive and negative values ‘box-cox’, only works with strictly positive values. Choices are {"yeo-johnson", "box-cox"}|
+|*Default:*|"yeo-johnson"|
+|*Type:*|choice|
+
+  - **powertransformer_standardize**  
   
 |||
 |-|-|
-|*Description:*|Weight given to training set response values in UMAP projection, if UMAP transformation is requested. Can be input as a comma separated list for hyperparameter search (e.g. '0.0,0.1,0.2')|
-|*Default:*|0.0|
+|*Description:*|Set to True to apply zero-mean, unit-variance normalization to the transformed output.|
+|*Default:*|TRUE|
+|*Type:*|Bool|
+
+  - **imputer_strategy**  
   
+|||
+|-|-|
+|*Description:*|This sets the imputer strategy for the SimpleImputer for use with PowerTransformer or RobustScaler. Choices are {"mean", "median", "most_frequent"}|
+|*Default:*|"mean"|
+|*Type:*|choice|
+
+
 ---
 
 <a name="XGBoost"></a>
@@ -903,6 +920,14 @@ in the same way as model parameters.
 |*Description:*|Computational system you are running on, LC or twintron-blue. LLNL system specific|
 |*Default:*|twintron-blue|
 |*Type:*|str|
+
+- **max_invalid_pred_frac**  
+  
+|||
+|-|-|
+|*Description:*|The amount of invalid (NaN/Inf) predictions acceptable for filtering out of the dataset before calculating model performance metrics, as a fraction from 0-1.|
+|*Default:*|0.01|
+|*Type:*|float|
   
 ---
 
@@ -1088,7 +1113,7 @@ in the same way as model parameters.
 ## Bayesian Optimization  
 ### Search Domain Specifications
 The following parameters are used to specify the search domains for certain model parameters in a Bayesian hyperparameter optimization. Each search domain parameter is
-tied to a specific model parameter. Only a subset of model parameters may be optimized in this way, but more will be supported in future releases. See the hyperopt package documentation at https://github.com/hyperopt/hyperopt/wiki/FMin#2-defining-a-search-space to learn more about the search domain format.
+tied to a specific model parameter. Only a subset of model parameters may be optimized in this way, but more will be supported in future releases. See the Optuna documentation at https://optuna.readthedocs.io/en/stable/reference/generated/optuna.trial.Trial.html to learn more about the search domain format.
 
 - **lr**  
   
diff --git a/atomsci/ddm/docs/source/advanced/advanced_installation.rst b/atomsci/ddm/docs/source/advanced/advanced_installation.rst
index c802365a..aadab39c 100644
--- a/atomsci/ddm/docs/source/advanced/advanced_installation.rst
+++ b/atomsci/ddm/docs/source/advanced/advanced_installation.rst
@@ -9,15 +9,7 @@ Deployment
 
 * Red Hat Enterprise Linux 7 with SLURM
 * Ubuntu 16.04
- 
-Uninstallation
---------------
-To remove AMPL from a pip environment use:
-::
 
-    deactivate
-    pip uninstall atomsci-ampl
- 
 
 To remove the atomsci pip environment entirely from a system use:
 ::
diff --git a/atomsci/ddm/docs/source/conf.py b/atomsci/ddm/docs/source/conf.py
index 7cafdd40..0045ed0b 100644
--- a/atomsci/ddm/docs/source/conf.py
+++ b/atomsci/ddm/docs/source/conf.py
@@ -23,12 +23,12 @@
 
 project = 'ATOM Data-Driven Modeling Pipeline'
 author = 'ATOM DDM Team'
- 
+
 #note: wait on this
 # import atomsci.ddm
 #version = atomsci.ddm.__version__
 # The short X.Y version
-version = '1.7.0'
+version = '1.8.0'
 copyright = f'{datetime.datetime.now().year}, {author}'
 
 # The full version, including alpha/beta/rc tags
@@ -195,5 +195,10 @@
 
 
 # -- Extension configuration -------------------------------------------------
-autodoc_mock_imports = ["rdkit", "tensorflow.contrib", "atomsci", "yaml", "sklearn.preprocessing.Imputer"]
-
+autodoc_mock_imports = [
+    "rdkit", 
+    "tensorflow.contrib", 
+    "atomsci", 
+    "yaml", 
+    "sklearn.preprocessing.Imputer"
+]
diff --git a/atomsci/ddm/docs/source/get_started/install.rst b/atomsci/ddm/docs/source/get_started/install.rst
index 7e577a09..f92c9e31 100644
--- a/atomsci/ddm/docs/source/get_started/install.rst
+++ b/atomsci/ddm/docs/source/get_started/install.rst
@@ -3,39 +3,61 @@
 Installation
 ============
 
-Setup Repo, Pip Environment
----------------------------
+Python Environment
+------------------
 
-Clone git repository::
+AMPL requires Python 3.10.
 
-    git clone https://github.com/ATOMScience-org/AMPL.git
- 
-Please refer to this `install link <https://github.com/ATOMScience-org/AMPL#Install>`_, for details.
-
-Create pip environment::
+For LLNL users on LC::
 
-    module load python/3.9.12 # use python 3.9.12
-    python3 -m venv atomsci-env # create a new pip env
-    source atomsci-env/bin/activate # activate the environemt
+    module load python/3.10.8
 
-    python3 -m pip install pip --upgrade
-    cd $AMPL_HOME/pip # cd to AMPL repo's pip directory
+Create and activate a virtual environment::
 
-    pip3 install --force-reinstall -r requirements.txt
+    make sync-<platform>                  # for example: make sync-cpu
+    source .venv-<platform>/bin/activate
 
 .. note::
 
    Depending on system performance, creating the environment can take some time.
 
-Build and Install AMPL
+Install AMPL from PyPI
 ----------------------
-Go to the AMPL root directory and install the `AMPL <https://github.com/ATOMScience-org/AMPL>`_  package::
 
-    source atomsci-env/bin/activate # activate the environemt
-    cd ..
+AMPL is published to PyPI, so most users can install it directly without cloning the repository or building it locally::
+
+    uv pip install atomsci-ampl
+
+This installs the released AMPL package into your active virtual environment.
+
+Install AMPL from a Local Clone
+-------------------------------
+
+If you want to work from the source code, first clone the repository::
+
+    git clone https://github.com/ATOMScience-org/AMPL.git
+    cd AMPL
+
+Then choose one of the following workflows.
+
+Development Install
+~~~~~~~~~~~~~~~~~~~
+
+For local development, install AMPL from the source tree in editable mode::
+
+    ./install-dev.sh
+
+This is the recommended workflow for development. Changes to the local source code are picked up without reinstalling the package.
+
+Build and Install Locally
+~~~~~~~~~~~~~~~~~~~~~~~~~
+
+If you want to build the package locally and install the built artifact::
+
     ./build.sh
-    pip3 install -e .
+    ./install.sh
 
-* The `install.sh` system command installs AMPL directly in the pip environment. If `install.sh` alone is used, then AMPL is installed in the `$HOME/.local` directory.
+In this workflow:
 
-* After this process, you will have an `atomsci-env` pip environment with all dependencies installed. The name of the AMPL package is `atomsci-ampl` and is installed in the `install.sh` script to the environment with pip.  
+* ``build.sh`` builds the package artifacts locally.
+* ``install.sh`` installs the locally built package into the active environment.
\ No newline at end of file
diff --git a/atomsci/ddm/docs/source/get_started/introduction.rst b/atomsci/ddm/docs/source/get_started/introduction.rst
index d9fc08a8..be3e06b4 100644
--- a/atomsci/ddm/docs/source/get_started/introduction.rst
+++ b/atomsci/ddm/docs/source/get_started/introduction.rst
@@ -3,10 +3,25 @@
 Introduction
 ************
 
-The ATOM Modeling PipeLine `AMPL <https://github.com/ATOMScience-org/AMPL>`_ is a Python 3 package that has been developed and run in a specific pip environment.
+The ATOM Modeling PipeLine, `AMPL <https://github.com/ATOMScience-org/AMPL>`_, is a Python 3 package.
 
-There are two alternative paths for installing `AMPL <https://github.com/ATOMScience-org/AMPL>`_: 
+There are two main ways to use `AMPL <https://github.com/ATOMScience-org/AMPL>`_:
 
-1. Cloning the git repo, setting up the pip environment and running the AMPL install command. Please refer to `Installation <https://ampl.readthedocs.io/en/latest/get_started/install.html>`_.
+1. Install the AMPL Python package from PyPI, or clone the Git repository for local development.
 
-2. `Install With Docker <https://ampl.readthedocs.io/en/latest/get_started/install_with_docker.html>`_. 
+   To install the released package from PyPI:
+
+   .. code-block:: bash
+
+      uv pip install atomsci-ampl
+
+   To work from a local source checkout instead:
+
+   .. code-block:: bash
+
+      git clone https://github.com/ATOMScience-org/AMPL.git
+      cd AMPL
+
+   Please refer to `Installation <https://ampl.readthedocs.io/en/latest/get_started/install.html>`_.
+
+2. `Install With Docker <https://ampl.readthedocs.io/en/latest/get_started/install_with_docker.html>`_.
\ No newline at end of file
diff --git a/atomsci/ddm/docs/source/tutorials/05_hyperopt.rst b/atomsci/ddm/docs/source/tutorials/05_hyperopt.rst
index b12432a7..f4fa5d85 100644
--- a/atomsci/ddm/docs/source/tutorials/05_hyperopt.rst
+++ b/atomsci/ddm/docs/source/tutorials/05_hyperopt.rst
@@ -41,7 +41,7 @@ functions:
 
 -  `parse_params <https://ampl.readthedocs.io/en/latest/utils.html#utils.hyperparam_search_wrapper.parse_params>`_
 -  `build_search <https://ampl.readthedocs.io/en/latest/utils.html#utils.hyperparam_search_wrapper.build_search>`_
--  `run_search <https://ampl.readthedocs.io/en/latest/utils.html#utils.hyperparam_search_wrapper.HyperOptSearch.run_search>`_
+-  `run_search <https://ampl.readthedocs.io/en/latest/utils.html#utils.hyperparam_search_wrapper.OptunaSearch.run_search>`_
 -  `get_filesystem_perf_results <https://ampl.readthedocs.io/en/latest/pipeline.html#pipeline.compare_models.get_filesystem_perf_results>`_
 
 The first three functions in the above list come from the
@@ -250,7 +250,7 @@ Below are some parameters that can be used for **neural networks**,
 set of parameters can be used to replace the parameters above. Trying
 them out is left as an exercise for the reader.
 
-Neural Network Hyperopt Search
+Neural Network Optuna Search
 ------------------------------
 
 .. list-table::
diff --git a/atomsci/ddm/docs/source/utils.rst b/atomsci/ddm/docs/source/utils.rst
index 6ad9db91..d5d01353 100644
--- a/atomsci/ddm/docs/source/utils.rst
+++ b/atomsci/ddm/docs/source/utils.rst
@@ -36,6 +36,14 @@ utils.datastore\_functions module
    :undoc-members:
    :show-inheritance:
 
+utils.generate\_transformers module
+-----------------------------------
+
+.. automodule:: utils.generate_transformers
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
 utils.hyperparam\_search\_wrapper module
 ----------------------------------------
 
diff --git a/atomsci/ddm/examples/tutorials/05_hyperopt.ipynb b/atomsci/ddm/examples/tutorials/05_hyperopt.ipynb
index 388c8860..64ce8622 100644
--- a/atomsci/ddm/examples/tutorials/05_hyperopt.ipynb
+++ b/atomsci/ddm/examples/tutorials/05_hyperopt.ipynb
@@ -27,7 +27,7 @@
     "We will use these **[AMPL](https://github.com/ATOMScience-org/AMPL)** functions:\n",
     "- [parse_params](https://ampl.readthedocs.io/en/latest/utils.html#utils.hyperparam_search_wrapper.parse_params)\n",
     "- [build_search](https://ampl.readthedocs.io/en/latest/utils.html#utils.hyperparam_search_wrapper.build_search)\n",
-    "- [run_search](https://ampl.readthedocs.io/en/latest/utils.html#utils.hyperparam_search_wrapper.HyperOptSearch.run_search)\n",
+    "- [run_search](https://ampl.readthedocs.io/en/latest/utils.html#utils.hyperparam_search_wrapper.OptunaSearch.run_search)\n",
     "- [get_filesystem_perf_results](https://ampl.readthedocs.io/en/latest/pipeline.html#pipeline.compare_models.get_filesystem_perf_results)\n",
     "\n",
     "The first three functions in the above list come from the `hyperparameter_search_wrapper` module. "
@@ -496,7 +496,7 @@
     "Each set of parameters can be used to replace the parameters above. \n",
     "Trying them out is left as an exercise for the reader.\n",
     "\n",
-    "#### Neural Network Hyperopt Search\n",
+    "#### Neural Network Optuna Search\n",
     "\n",
     "|Parameter|Description|\n",
     "|---|---|\n",
diff --git a/atomsci/ddm/pipeline/compare_models.py b/atomsci/ddm/pipeline/compare_models.py
index 8f55d9be..de496a04 100644
--- a/atomsci/ddm/pipeline/compare_models.py
+++ b/atomsci/ddm/pipeline/compare_models.py
@@ -691,202 +691,6 @@ def get_best_models_info(col_names=None, bucket='public', pred_type="regression"
                 top_models_df.to_csv(os.path.join(output_dir, 'best_models_metadata_%s.csv' % shortened_key), index=False)
     return top_models_df
 
-
-# TODO: This function looks like work in progress, should we delete it?
-'''
-#---------------------------------------------------------------------------------------------------------
-def _get_best_grouped_models_info(collection='pilot_fixed', pred_type='regression', top_n=1, subset='test'):
-    """Get results for models in the given collection."""
-
-    if not mlmt_supported:
-        print("Model tracker not supported in your environment; can examine models saved in filesystem only.")
-        return
-
-    res_dir = '/usr/local/data/%s_perf' % collection
-    plt_dir = '%s/Plots' % res_dir
-    os.makedirs(plt_dir, exist_ok=True)
-    res_files = os.listdir(res_dir)
-    suffix = '_%s_model_perf_metrics.csv' % collection
-
-    if pred_type == 'regression':
-        metric_type = 'r2_score'
-    else:
-        metric_type = 'roc_auc_score'
-    for res_file in res_files:
-        try:
-            if not res_file.endswith(suffix):
-                continue
-            res_path = os.path.join(res_dir, res_file)
-
-            res_df = pd.read_csv(res_path, index_col=False)
-            res_df['combo'] = ['%s/%s' % (m,f) for m, f in zip(res_df.model_type.values, res_df.featurizer.values)]
-            dset_name = res_file.replace(suffix, '')
-            datasets.append(dset_name)
-            res_df['dataset'] = dset_name
-            print(dset_name)
-            res_df = res_df.sort_values('{0}_{1}'.format(metric_type, subset), ascending=False)
-            res_df['model_type/feat'] = ['%s/%s' % (m,f) for m, f in zip(res_df.model_type.values, res_df.featurizer.values)]
-            res_df = res_df.sort_values('{0}_{1}'.format(metric_type, subset), ascending=False)
-            grouped_df = res_df.groupby('model_type/feat').apply(
-                lambda t: t.head(top_n)
-            ).reset_index(drop=True)
-            top_grouped_models.append(grouped_df)
-            top_combo = res_df['model_type/feat'].values[0]
-            top_combo_dsets.append(top_combo + dset_name.lstrip('ATOM_GSK_dskey'))
-            top_score = res_df['{0}_{1}'.format(metric_type, subset)].values[0]
-            top_model_feat.append(top_combo)
-            top_scores.append(top_score)
-            num_samples.append(res_df['Dataset Size'][0])
-
-#------------------------------------------------------------------------------------------------------------------
-def get_umap_nn_model_perf_table(dataset_key, bucket, collection_name, pred_type='regression'):
-    """Load performance metrics from model tracker for all NN models with the given prediction_type saved in
-    the model tracker DB under a given collection that were trained against a particular dataset. Show
-    parameter settings for UMAP transformer for models where they are available.
-
-    Args:
-        dataset_key (str): Dataset key for training dataset.
-
-        bucket (str): Dataset bucket for training dataset.
-
-        collection_name (str): Name of model tracker collection to search for models.
-
-        pred_type (str): Prediction type ('classification' or 'regression') of models to query.
-
-    Returns:
-        pd.DataFrame: Table of model performance metrics.
-
-    """
-    if not mlmt_supported:
-        print("Model tracker not supported in your environment; can examine models saved in filesystem only.")
-        return None
-
-    query_params = {
-        "match_metadata": {
-            "training_dataset.bucket": bucket,
-            "training_dataset.dataset_key": dataset_key,
-            "model_parameters.model_type" : "NN",
-            "model_parameters.prediction_type" : pred_type
-        },
-
-        "match_metrics": {
-            "metrics_type": "training",  # match only training metrics
-            "label": "best",
-        },
-    }
-    query_params['match_metadata'].update(other_filters)
-
-    print("Finding models trained on %s dataset %s" % (bucket, dataset_key))
-    mlmt_client = dsf.initialize_model_tracker()
-    metadata_list = mlmt_client.model.query_model_metadata(
-        collection_name=collection_name,
-        query_params=query_params,
-    ).result()
-    if metadata_list == []:
-        print("No matching models returned")
-        return
-    else:
-        print("Found %d matching models" % len(metadata_list))
-
-    model_uuid_list = []
-    learning_rate_list = []
-    dropouts_list = []
-    layer_sizes_list = []
-    featurizer_list = []
-    best_epoch_list = []
-    max_epochs_list = []
-
-    feature_transform_type_list = []
-    umap_dim_list = []
-    umap_targ_wt_list = []
-    umap_neighbors_list = []
-    umap_min_dist_list = []
-
-    subsets = ['train', 'valid', 'test']
-
-    if pred_type == 'regression':
-        sort_metric = 'r2_score'
-        metrics = ['r2_score', 'rms_score', 'mae_score']
-    else:
-        sort_metric = 'roc_auc_score'
-        metrics = ['roc_auc_score', 'prc_auc_score', 'matthews_cc', 'kappa', 'confusion_matrix']
-    score_dict = {}
-    for subset in subsets:
-        score_dict[subset] = {}
-        for metric in metrics:
-            score_dict[subset][metric] = []
-
-    for metadata_dict in metadata_list:
-        model_uuid = metadata_dict['model_uuid']
-        #print("Got metadata for model UUID %s" % model_uuid)
-
-        # Get model metrics for this model
-        metrics_dicts = metadata_dict['training_metrics']
-        #print("Got %d metrics dicts for model %s" % (len(metrics_dicts), model_uuid))
-        if len(metrics_dicts) < 3:
-            print("Got no or incomplete metrics for model %s, skipping..." % model_uuid)
-            continue
-        if len(metrics_dicts) > 3:
-            raise Exception('Got more than one set of best epoch metrics for model %s' % model_uuid)
-        subset_metrics = {}
-        for metrics_dict in metrics_dicts:
-            subset = metrics_dict['subset']
-            subset_metrics[subset] = metrics_dict['prediction_results']
-
-        model_uuid_list.append(model_uuid)
-        model_params = metadata_dict['model_parameters']
-        model_type = model_params['model_type']
-        if model_type != 'NN':
-            continue
-        featurizer = model_params['featurizer']
-        featurizer_list.append(featurizer)
-        feature_transform_type = metadata_dict['training_dataset']['feature_transform_type']
-        feature_transform_type_list.append(feature_transform_type)
-        nn_params = metadata_dict['nn_specific']
-        max_epochs_list.append(nn_params['max_epochs'])
-        best_epoch_list.append(nn_params['best_epoch'])
-        learning_rate_list.append(nn_params['learning_rate'])
-        layer_sizes_list.append(','.join(['%d' % s for s in nn_params['layer_sizes']]))
-        dropouts_list.append(','.join(['%.2f' % d for d in nn_params['dropouts']]))
-        for subset in subsets:
-            for metric in metrics:
-                score_dict[subset][metric].append(subset_metrics[subset][metric])
-        if 'umap_specific' in metadata_dict:
-            umap_params = metadata_dict['umap_specific']
-            umap_dim_list.append(umap_params['umap_dim'])
-            umap_targ_wt_list.append(umap_params['umap_targ_wt'])
-            umap_neighbors_list.append(umap_params['umap_neighbors'])
-            umap_min_dist_list.append(umap_params['umap_min_dist'])
-        else:
-            umap_dim_list.append(nan)
-            umap_targ_wt_list.append(nan)
-            umap_neighbors_list.append(nan)
-            umap_min_dist_list.append(nan)
-
-
-    perf_df = pd.DataFrame(dict(
-                    model_uuid=model_uuid_list,
-                    learning_rate=learning_rate_list,
-                    dropouts=dropouts_list,
-                    layer_sizes=layer_sizes_list,
-                    featurizer=featurizer_list,
-                    best_epoch=best_epoch_list,
-                    max_epochs=max_epochs_list,
-                    feature_transform_type=feature_transform_type_list,
-                    umap_dim=umap_dim_list,
-                    umap_targ_wt=umap_targ_wt_list,
-                    umap_neighbors=umap_neighbors_list,
-                    umap_min_dist=umap_min_dist_list ))
-    for subset in subsets:
-        for metric in metrics:
-            metric_col = '%s_%s' % (metric, subset)
-            perf_df[metric_col] = score_dict[subset][metric]
-    sort_by = '%s_valid' % sort_metric
-
-    perf_df = perf_df.sort_values(sort_by, ascending=False)
-    return perf_df
-'''
-
 #------------------------------------------------------------------------------------------------------------------
 def get_tarball_perf_table(model_tarball, pred_type='classification'):
     """Retrieve model metadata and performance metrics for a model saved as a tarball (.tar.gz) file.
@@ -966,10 +770,16 @@ def get_filesystem_perf_results(result_dir, pred_type='classification', expand=T
     subsets = ['train', 'valid', 'test']
 
     if pred_type == 'regression':
-        metrics = ['r2_score', 'rms_score', 'mae_score', 'num_compounds']
+        metrics = [
+            'r2_score', 'rms_score', 'mae_score', 'num_compounds',
+            'invalid_prediction_count', 'invalid_prediction_fraction', 'invalid_prediction_threshold'
+        ]
     else:
-        metrics = ['roc_auc_score', 'prc_auc_score', 'precision', 'recall_score', 'num_compounds',
-                   'accuracy_score', 'bal_accuracy', 'npv', 'matthews_cc', 'kappa', 'cross_entropy', 'confusion_matrix']
+        metrics = [
+            'roc_auc_score', 'prc_auc_score', 'precision', 'recall_score', 'num_compounds',
+            'accuracy_score', 'bal_accuracy', 'npv', 'matthews_cc', 'kappa', 'cross_entropy', 'confusion_matrix',
+            'invalid_prediction_count', 'invalid_prediction_fraction', 'invalid_prediction_threshold'
+        ]
     score_dict = {}
     for subset in subsets:
         score_dict[subset] = {}
@@ -1864,9 +1674,22 @@ def get_multitask_perf_from_files_new(result_dir, pred_type='regression', datase
                     model_list.append(meta)
 
     print(f'Found data for {len(model_list)} {pred_type} models under {result_dir}')
+    if len(model_list) == 0:
+        logger.warning(
+            "No %s models found under %s for dataset_key=%s",
+            pred_type,
+            result_dir,
+            dataset_key,
+        )
+        return pd.DataFrame()
     
     # unpack metadata dicts
     metadata=pd.DataFrame(model_list)
+
+    # Ensure optional columns are present for backward compatibility with older metadata.
+    for req_col in ['model_uuid', 'time_built', 'model_path', 'training_metrics', 'seed']:
+        if req_col not in metadata.columns:
+            metadata[req_col] = np.nan
     
     # establish initial unpacked models df
     dropcols=['model_uuid','time_built','model_path','training_metrics','seed']
@@ -1876,13 +1699,13 @@ def get_multitask_perf_from_files_new(result_dir, pred_type='regression', datase
     dict_cols=['model_uuid','splitting_parameters']
     dict_cols.extend([x for x in metadata.columns if 'specific' in x and (x!='descriptor_specific')])
     dropcols.extend([x for x in metadata.columns if ('specific' in x) and (x!='descriptor_specific')])
-    keep_dicts=metadata[dict_cols]       
+    keep_dicts=metadata[[c for c in dict_cols if c in metadata.columns]]       
     
     # extract training data
     training_metrics=metadata[['model_uuid','training_metrics']]
     
     # drop info from metadata
-    metadata=metadata.drop(columns=dropcols)
+    metadata=metadata.drop(columns=dropcols, errors='ignore')
     
     # unpack and re-merge simple dicts into models df
     for col in metadata.columns:
@@ -1925,8 +1748,32 @@ def get_multitask_perf_from_files_new(result_dir, pred_type='regression', datase
 
             # get task scores - long form and rename columns
             taskcols=['response_cols']
-            taskcols.extend([x for x in pred.columns if 'task' in x])    
-            task_preds=pred[['model_uuid']+taskcols].set_index('model_uuid').explode(taskcols).reset_index()
+            taskcols.extend([x for x in pred.columns if 'task' in x])
+
+            task_preds = pred[['model_uuid'] + taskcols].copy()
+
+            def _num_tasks_for_row(resp_cols):
+                if isinstance(resp_cols, list):
+                    return len(resp_cols)
+                return 1
+
+            def _normalize_task_col_value(value, n_tasks):
+                if isinstance(value, list):
+                    if len(value) == n_tasks:
+                        return value
+                    if len(value) == 1 and n_tasks > 1:
+                        return value * n_tasks
+                    return value[:n_tasks] + [np.nan] * max(0, n_tasks - len(value))
+                if pd.isna(value):
+                    return [np.nan] * n_tasks
+                return [value] * n_tasks
+
+            for ridx in task_preds.index:
+                n_tasks = _num_tasks_for_row(task_preds.at[ridx, 'response_cols'])
+                for tcol in [c for c in taskcols if c != 'response_cols']:
+                    task_preds.at[ridx, tcol] = _normalize_task_col_value(task_preds.at[ridx, tcol], n_tasks)
+
+            task_preds = task_preds.set_index('model_uuid').explode(taskcols).reset_index()
         
             # get full model scores and rename columns
             predcols=[x for x in pred.columns if 'task' not in x]
@@ -1937,9 +1784,44 @@ def get_multitask_perf_from_files_new(result_dir, pred_type='regression', datase
         
             # rename task_pred columns to match full model names
             coldict={}
+            task_to_full_overrides = {
+                'task_r2_scores': 'r2_score',
+                'task_rms_scores': 'rms_score',
+                'task_mae_scores': 'mae_score',
+                'task_roc_auc_scores': 'roc_auc_score',
+                'task_roc_auc_stds': 'roc_auc_std',
+                'task_prc_auc_scores': 'prc_auc_score',
+                'task_cross_entropies': 'cross_entropy',
+                'task_precisions': 'precision',
+                'task_recalls': 'recall_score',
+                'task_npvs': 'npv',
+                'task_accuracies': 'accuracy_score',
+                'task_bal_accuracies': 'bal_accuracy',
+                'task_kappas': 'kappa',
+                'task_matthews_ccs': 'matthews_cc',
+                'task_invalid_prediction_counts': 'invalid_prediction_count',
+                'task_invalid_prediction_fractions': 'invalid_prediction_fraction',
+            }
             for task_col in task_preds.columns:
                 if task_col not in ['model_uuid','response_cols']:
-                    coldict[task_col]=[predcol for predcol in pred.columns if predcol.replace(metlabel+'_','').startswith(task_col.replace('task_','')[0:3])][0]
+                    if task_col in task_to_full_overrides:
+                        target_metric = task_to_full_overrides[task_col]
+                    else:
+                        target_metric = task_col.replace('task_', '')
+
+                    pred_col = metlabel + '_' + target_metric
+                    if pred_col not in pred.columns:
+                        # Fallback to original prefix matching for unexpected/new metric naming.
+                        prefix = task_col.replace('task_', '')[0:3]
+                        candidates = [
+                            pcol for pcol in pred.columns
+                            if pcol.replace(metlabel + '_', '').startswith(prefix)
+                        ]
+                        if len(candidates) == 0:
+                            continue
+                        pred_col = candidates[0]
+
+                    coldict[task_col] = pred_col
             task_preds=task_preds.rename(columns=coldict)
         
             # concatenate all scores
diff --git a/atomsci/ddm/pipeline/featurization.py b/atomsci/ddm/pipeline/featurization.py
index 27e601b5..a7379d82 100644
--- a/atomsci/ddm/pipeline/featurization.py
+++ b/atomsci/ddm/pipeline/featurization.py
@@ -23,6 +23,10 @@
 from rdkit.Chem import Descriptors
 from rdkit.ML.Descriptors import MoleculeDescriptors
 
+from sklearn.preprocessing import RobustScaler, PowerTransformer
+from sklearn.feature_selection import VarianceThreshold
+from sklearn.impute import SimpleImputer
+from sklearn.pipeline import Pipeline
 subclassed_mordred_classes = ['EState', 'MolecularDistanceEdge']
 try:
     from mordred import Calculator, descriptors
@@ -111,7 +115,6 @@ def copy_featurizer_params(source, dest):
     """Copy all parameters related to featurization from source to target params
     Returns a deepcopy of dest object with featurization parameters copied from
     source object
-    
     Args:
         source (argparse.Namespace): Object containing source parameters
         dest (argparse.Namespace): Destination object for parameters
@@ -349,7 +352,7 @@ def compute_all_mordred_descrs(mols, max_cpus=None, quiet=True):
     res_df = calc.pandas(mols, quiet=quiet, nproc=max_cpus)
     log.debug("Done computing Mordred descriptors")
     try:
-        res_df = res_df.fill_missing().applymap(float)
+        res_df = res_df.fill_missing().map(float)
         return res_df
     except ValueError:
         log.error('Mordred descriptor calculation failed in MordredDataFrame.fill_missing')
@@ -492,14 +495,13 @@ def compute_all_moe_descriptors(smiles_df, params):
         string prepared by MOE, a sequence index, and columns for each MOE descriptor.
     """
 
-    # TODO: Get MOE_PATH from params
     moe_path = os.environ.get('MOE_PATH', '/usr/workspace/atom/moe2022_site/bin')
     if not os.path.exists(moe_path):
         raise Exception("MOE is not available, or MOE_PATH environment variable needs to be set.")
-    moe_root = os.path.abspath('%s/..' % moe_path)
-    # Make sure we have an environment variable that points to the license server
-    if os.environ.get('LM_LICENSE_FILE', None) is None:
-        os.environ['LM_LICENSE_FILE'] = '7002@bribe.llnl.gov'
+    
+    # Set MOE_SVL_ROOT
+    moe_svl_root = os.path.join(os.path.dirname(os.path.dirname(__file__)),
+        'data', 'moe_svl')
 
     moe_args = []
     moe_args.append("{moePath}/moebatch".format(moePath=moe_path))
@@ -515,8 +517,8 @@ def compute_all_moe_descriptors(smiles_df, params):
     # TODO: Directory with svl scripts should be part of AMPL installation. The code below is specific to the LC environment.
     moe_template = """db_Close db_Open['{fileMDB}','create']; db_ImportASCII[ascii_file: '{smilesFile}',
     db_file: '{fileMDB}',delimiter: ',', quotes: 0, names: ['original_smiles','cmpd_id'],types: ['char','char']];
-    run ['{moeRoot}/custom/ksm_svl/smp_WashMinimizeSMILES.svl', ['{fileMDB}', 'original_smiles']];
-    run ['{moeRoot}/custom/svl/db_desc_smp5.svl',['{fileMDB}','mol_prep', [], [codeset: 'All_No_MOPAC_Protein']]];
+    run ['{moe_svl_root}/custom/ksm_svl/smp_WashMinimizeSMILES.svl', ['{fileMDB}', 'original_smiles']];
+    run ['{moe_svl_root}/custom/svl/db_desc_smp5.svl',['{fileMDB}','mol_prep', [], [codeset: 'All_No_MOPAC_Protein']]];
     dir_export_ASCIIBB ['{fileMDB}',[quotes:1,titles:1]];"""
 
     #with tempfile.TemporaryDirectory() as tmpdir:
@@ -529,7 +531,7 @@ def compute_all_moe_descriptors(smiles_df, params):
         smiles_df.to_csv(smiles_file, index=False, columns=[params.smiles_col, params.id_col])
         log.debug("Wrote SMILES strings to %s" % smiles_file)
         os.chdir(tmpdir)
-        moe_cmds = '"' + moe_template.format(moeRoot=moe_root, smilesFile=smiles_file, fileMDB=file_mdb) + '"'
+        moe_cmds = '"' + moe_template.format(moe_svl_root=moe_svl_root, smilesFile=smiles_file, fileMDB=file_mdb) + '"'
         moe_args.append(moe_cmds)
         moe_args.append("-exit")
         log.debug('Computing MOE descriptors')
@@ -639,12 +641,13 @@ def get_feature_count(self):
         raise NotImplementedError
 
     # ****************************************************************************************
-    def create_feature_transformer(self, dataset):
+    def create_feature_transformer(self, dataset, params):
         """Fit a scaling and centering transformation to the feature matrix of the given dataset, and return a
         DeepChem transformer object holding its parameters.
 
         Args:
             dataset (deepchem.Dataset): featurized dataset
+            params (Namespace): Contains parameters used to instantiate the featurizer.
 
         Returns:
             Empty list
@@ -838,12 +841,13 @@ def featurize_data(self, dset_df, params, contains_responses):
         return features, ids, vals, attr, w, featurized_dset_df
 
     # ****************************************************************************************
-    def create_feature_transformer(self, dataset):
+    def create_feature_transformer(self, dataset, params):
         """Fit a scaling and centering transformation to the feature matrix of the given dataset, and return a
         DeepChem transformer object holding its parameters.
 
         Args:
             dataset (deepchem.Dataset): featurized dataset
+            params (Namespace): Contains parameters used to instantiate the featurizer.
 
         Returns:
             Empty list since we will not be transforming the features of a DynamicFeaturization object
@@ -1185,12 +1189,13 @@ def featurize_data(self, dset_df, params, contains_responses):
         raise NotImplementedError
 
     # ****************************************************************************************
-    def create_feature_transformer(self, dataset):
+    def create_feature_transformer(self, dataset, params):
         """Fit a scaling and centering transformation to the feature matrix of the given dataset, and return a
         DeepChem transformer object holding its parameters.
 
         Args:
             dataset (deepchem.Dataset): featurized dataset
+            params (Namespace): Contains parameters used to instantiate the featurizer.
 
         """
         # Leave it to subclasses to determine if features should be scaled and centered.
@@ -1623,17 +1628,60 @@ def get_feature_count(self):
         return len(self.get_feature_columns())
 
     # ****************************************************************************************
-    def create_feature_transformer(self, dataset):
+    def create_feature_transformer(self, dataset, params):
         """Fit a scaling and centering transformation to the feature matrix of the given dataset, and return a
         DeepChem transformer object holding its parameters.
 
         Args:
             dataset (deepchem.Dataset): featurized dataset
+            params (Namespace): Contains parameters used to instantiate the featurizer.
 
         Returns:
             (list of DeepChem transformer objects): list of transformers for the feature matrix
         """
-        transformers_x = [trans.NormalizationTransformerMissingData(transform_X=True, dataset=dataset)]
+        if params.feature_transform_type == 'normalization':
+            transformers_x = [trans.NormalizationTransformerMissingData(transform_X=True, dataset=dataset)]
+        elif params.feature_transform_type == 'RobustScaler':
+            # keep_empty_features is set to true so that feature counts do not change
+            imputer = SimpleImputer(strategy=params.imputer_strategy, keep_empty_features=True)
+            # Check quartile_range has length 2 and convert it into a tuple
+            quartiles = params.robustscaler_quartile_range
+            assert len(quartiles) == 2, f'robustscaler_quartile_range must have length 2, got {quartiles}.'
+            quartiles = (quartiles[0], quartiles[1])
+            robust_scaler = RobustScaler(
+                        with_centering=params.robustscaler_with_centering,
+                        with_scaling=params.robustscaler_with_scaling,
+                        quantile_range=quartiles,
+                        unit_variance=params.robustscaler_unit_variance
+                    )
+            pipeline = Pipeline([('SimpleImputer', imputer), 
+                                 ('VarianceThreshold', VarianceThreshold(threshold=0.0)),
+                                 ('RobustScaler', robust_scaler)])
+            transformers_x = [
+                trans.SklearnPipelineWrapper(transform_X=True, dataset=dataset,
+                    sklearn_pipeline=pipeline)
+            ]
+        elif params.feature_transform_type == 'PowerTransformer':
+            # keep_empty_features is set to true so that feature counts do not change
+            imputer = SimpleImputer(strategy=params.imputer_strategy, keep_empty_features=True)
+            power_transformer = PowerTransformer(
+                        method=params.powertransformer_method,
+                        standardize=params.powertransformer_standardize
+            )
+            pipeline = Pipeline([('SimpleImputer', imputer), 
+                                 ('VarianceThreshold', VarianceThreshold(threshold=0.0)),
+                                 ('PowerTransformer', power_transformer)])
+            transformers_x = [
+                trans.SklearnPipelineWrapper(transform_X=True, dataset=dataset,
+                    sklearn_pipeline=pipeline)
+            ]
+        elif params.feature_transform_type == 'Identity':
+            transformers_x = []
+        else:
+            raise ValueError((
+                "feature_transform_type must be normalization, RobustScaler, "
+                f"PowerTransformer, or Identity. Got {params.feature_transform_type}"))
+
         return transformers_x
 
 
@@ -1909,6 +1957,9 @@ def compute_descriptors(self, smiles_df, params):
             if not mordred_supported:
                 raise Exception("mordred package needs to be installed to use Mordred descriptors")
             desc_df, is_valid = self.compute_mordred_descriptors(smiles_df[params.smiles_col].values, params)
+            if descr_scaled:
+                desc_df = self.scale_by_heavyatomcount_and_log_scale(desc_df, params.descriptor_type)
+
             desc_df = desc_df[descr_cols]
             # Add the ID and SMILES columns to the returned data frame
             ret_df = smiles_df[is_valid][[params.id_col, params.smiles_col]].reset_index(drop=True)
@@ -1919,6 +1970,10 @@ def compute_descriptors(self, smiles_df, params):
 
         elif descr_source == 'rdkit':
             desc_df, is_valid = self.compute_rdkit_descriptors(smiles_df, smiles_col = params.smiles_col)
+            if descr_scaled:
+                desc_df = self.scale_by_heavyatomcount_and_log_scale(desc_df, params.descriptor_type)
+                # RDKit features do not contain log scale features
+
             desc_df = desc_df[descr_cols]
             # Add the ID and SMILES columns to the returned data frame
             ret_df = smiles_df[is_valid][[params.id_col, params.smiles_col]].reset_index(drop=True)
@@ -2042,6 +2097,61 @@ def scale_moe_descriptors(self, desc_df, descr_type):
         scaled_df=pd.concat(scaled_cols, axis=1)
         return scaled_df.copy()
 
+    # ****************************************************************************************
+    def scale_by_heavyatomcount_and_log_scale(self, desc_df, descr_type, heavy_atom_col=''):
+        """Scale selected descriptors computed by rdkit or mordred by dividing their values by the heavy atom count per molecule.
+
+        Args:
+            desc_df (DataFrame): Data frame containing computed descriptors.
+
+            descr_type (str): Descriptor type, used to look up expected set of descriptor columns.
+
+            heavy_atom_col (str): Column containing the heavy atom count. Default value is '', which will try
+                to infer the heavy atom count column based on descr_type.
+
+        Returns:
+            scaled_df (DataFrame): Data frame with scaled descriptors.
+
+        Raises:
+            ValueError: If there are negative feature values in a column that needs log scaling       
+
+        """
+        cls = self.__class__
+        descr_cols = cls.desc_type_cols[descr_type]
+        if heavy_atom_col != '':
+            ha_count = desc_df[heavy_atom_col].values
+        else:
+            if 'HeavyAtomCount' in descr_cols:
+                ha_count = desc_df.HeavyAtomCount.values
+            elif 'nHeavyAtom' in descr_cols:
+                ha_count = desc_df.nHeavyAtom.values
+            else:
+                raise RuntimeError("Could not infer heavy atom column. "
+                                   "Should be HeavyAtomCount or nHeavyAtom or set using the heavy_atom_col parameter")
+
+        unscaled_desc_cols = [col.replace('_per_heavyatom', '') for col in descr_cols]
+        unscaled_desc_cols = [col.replace('_log_scaled', '') for col in unscaled_desc_cols]
+        nondesc_cols = list(set(desc_df.columns.values) - set(unscaled_desc_cols))
+        scaled_cols=[desc_df[nondesc_cols].copy()]
+        for scaled_col, unscaled_col in zip(descr_cols, unscaled_desc_cols):
+            if scaled_col.endswith('_per_heavyatom'):
+                tmp_col=desc_df[unscaled_col] / ha_count
+                tmp_col=tmp_col.rename(scaled_col)
+                scaled_cols.append(tmp_col)
+            elif scaled_col.endswith('_log_scaled'):
+                feat_vals = desc_df[unscaled_col]
+                if any(feat_vals < 0):
+                    raise ValueError(f"Cannot log scale {unscaled_col} because of negative values in feature.")
+                tmp_col=np.log(feat_vals)
+                tmp_col=tmp_col.rename(scaled_col)
+                scaled_cols.append(tmp_col)
+            else:
+                tmp_col=desc_df[unscaled_col]
+                tmp_col=tmp_col.rename(scaled_col)
+                scaled_cols.append(tmp_col)
+        scaled_df=pd.concat(scaled_cols, axis=1)
+        return scaled_df.copy()
+
     # ****************************************************************************************
     def __str__(self):
         """Returns a human-readable description of this Featurization object.
diff --git a/atomsci/ddm/pipeline/model_datasets.py b/atomsci/ddm/pipeline/model_datasets.py
index cc7c18f1..a8d09c54 100644
--- a/atomsci/ddm/pipeline/model_datasets.py
+++ b/atomsci/ddm/pipeline/model_datasets.py
@@ -23,6 +23,25 @@
 logging.basicConfig(format='%(asctime)-15s %(message)s')
 
 
+# *****************************************************************************************
+def create_and_load_model_dataset(params, ds_client=None):
+    """Factory function for creating and loading a ModelDataset object.
+
+    Args:
+        params (Namespace object): contains all parameter information.
+
+        ds_client (Datastore client)
+    Returns:
+        (ModelDataset): instantiated ModelDataset subclass specified by params, with data loaded
+    """
+    featurization = feat.create_featurization(params)
+    dataset = create_model_dataset(params, featurization, ds_client=None)
+
+    # Load featurized data (this will use scaled_descriptors if available)
+    dataset.get_featurized_data()
+
+    return dataset
+
 # ****************************************************************************************
 def create_model_dataset(params, featurization, ds_client=None):
     """Factory function for creating DatastoreDataset or FileDataset objects.
@@ -1405,7 +1424,6 @@ def load_featurized_data(self):
             self.dataset_key = self.params.dataset_key
             return dset_df
 
-
         # Otherwise, generate the expected path for the featurized dataset
         featurized_dset_name = self.featurization.get_featurized_dset_name(self.dataset_name)
         dataset_dir = os.path.dirname(self.params.dataset_key)
diff --git a/atomsci/ddm/pipeline/model_pipeline.py b/atomsci/ddm/pipeline/model_pipeline.py
index 6061c427..59579ce1 100644
--- a/atomsci/ddm/pipeline/model_pipeline.py
+++ b/atomsci/ddm/pipeline/model_pipeline.py
@@ -380,6 +380,7 @@ def create_model_metadata(self):
             featurizer=self.params.featurizer,
             prediction_type=self.params.prediction_type,
             model_choice_score_type=self.params.model_choice_score_type,
+            max_invalid_pred_frac=self.params.max_invalid_pred_frac,
             num_model_tasks=self.params.num_model_tasks,
             class_number=self.params.class_number,
             transformers=self.params.transformers,
diff --git a/atomsci/ddm/pipeline/model_tracker.py b/atomsci/ddm/pipeline/model_tracker.py
index 61053c7e..9f6a3d85 100644
--- a/atomsci/ddm/pipeline/model_tracker.py
+++ b/atomsci/ddm/pipeline/model_tracker.py
@@ -459,9 +459,8 @@ def convert_metadata(old_metadata):
         ("descriptor_specific", 'DescriptorSpecific'),
         ("nn_specific", "NNSpecific"),
         ("xgb_specific", "xgbSpecific"),
-        ("umap_specific", "UmapSpecific"),
-
     ]
+
     for (nkey, okey) in map_keys:
         value = model_metadata.get(okey)
         if value is not None:
diff --git a/atomsci/ddm/pipeline/model_wrapper.py b/atomsci/ddm/pipeline/model_wrapper.py
index 63b96c7b..c4c45ecf 100644
--- a/atomsci/ddm/pipeline/model_wrapper.py
+++ b/atomsci/ddm/pipeline/model_wrapper.py
@@ -80,7 +80,7 @@ def get_latest_pytorch_checkpoint(model, model_dir=None):
     print(latest_chkpt)
 
     return latest_chkpt
- 
+
 
 def dc_restore(model, checkpoint=None, model_dir=None, session=None):
     """Reload the values of all variables from a checkpoint file.
@@ -391,7 +391,7 @@ def create_transformers(self, training_datasets):
         """Initialize transformers for responses, features and weights, and persist them for later.
 
         Args:
-            training_datasets: A dictionary of dc.Datasets containing the training data from 
+            training_datasets: A dictionary of dc.Datasets containing the training data from
             each fold. Generated using transformers.get_all_training_datasets.
 
         Side effects:
@@ -412,7 +412,10 @@ def create_transformers(self, training_datasets):
         for k, td in training_datasets.items():
             self.transformers[k] = self._create_output_transformers(td)
 
-            self.transformers_x[k] = self._create_feature_transformers(td)
+            if self.params.feature_transform_path is None:
+                self.transformers_x[k] = self._create_feature_transformers(td)
+            else:
+                self.transformers_x[k] = self.load_feature_transformers_from_pkl(self.params.feature_transform_path)
 
             # Set up transformers for weights, if needed
             self.transformers_w[k] = trans.create_weight_transformers(self.params, td)
@@ -442,7 +445,7 @@ def reload_transformers(self):
 
         # for backwards compatibity if this file exists, all folds use the same transformers
         local_path = f"{self.output_dir}/transformers.pkl"
-        
+
         if os.path.exists(local_path):
             self.log.info(f"Reloading transformers from model tarball {local_path}")
             with open(local_path, 'rb') as txfmr:
@@ -488,6 +491,40 @@ def reload_transformers(self):
 
         # ****************************************************************************************
 
+    def load_feature_transformers_from_pkl(self, transformer_pkl_path):
+        """Loads feature transformer from pkl path
+        This loads transformers_x from a pkl created using
+        generate_transformers.build_and_save_feature_transformers_from_csvs. This also
+        overwrites any parameters set when creating the transfomers saved at
+        transformer_pkl_path.
+
+        Args:
+            transformer_pkl_path (str): Path of saved transformer_pkl.
+
+        Returns:
+            None
+
+        """
+        with open(transformer_pkl_path, 'rb') as transformers_pkl_file:
+            saved_transformers = pickle.load(transformers_pkl_file)
+
+        # check that the loaded transformers are for the same features
+        if self.params.featurizer != saved_transformers['params']['featurizer']:
+            raise ValueError((f"Loaded transformers do not match featurizer. Expected {self.params.featurizer},"
+                             f" got {saved_transformers['params']['featurizer']}"))
+        if self.params.descriptor_type != saved_transformers['params']['descriptor_type']:
+            raise ValueError((f"Loaded transformers do not match descriptor_type. Expected {self.params.descriptor_type},"
+                             f" got {saved_transformers['params']['descriptor_type']}"))
+
+        # update self.params with the loaded transformer data
+        self.params.__dict__.update(saved_transformers['params'])
+
+        return saved_transformers['transformers_x']
+
+
+        # ****************************************************************************************
+
+
     def transform_dataset(self, dataset, fold):
         """Transform the responses and/or features in the given DeepChem dataset using the current transformers.
 
@@ -523,10 +560,7 @@ def get_num_features(self):
            the number of dimensions of the feature space, taking both featurization method
         and transformers into account.
         """
-        if self.params.feature_transform_type == 'umap':
-            return self.params.umap_dim
-        else:
-            return self.featurization.get_feature_count()
+        return self.featurization.get_feature_count()
 
         # ****************************************************************************************
 
@@ -705,7 +739,7 @@ def __init__(self, params, pipeline, log):
     # ****************************************************************************************
     def __iter__(self):
         return self
-        
+
     # ****************************************************************************************
     def __next__(self):
         """Returns epoch index or stops when the have been enough iterations."""
@@ -716,7 +750,7 @@ def __next__(self):
             # If we're running on an LC system, check that we have enough time to complete another epoch
             # before the current job finishes, by extrapolating from the time elapsed so far.
 
-            now = time.time() 
+            now = time.time()
             elapsed_time = now - self.start_time
             training_time = now - self.training_start
             time_remaining = self.time_limit * 60 - elapsed_time
@@ -938,8 +972,8 @@ def train_kfold_cv(self, pipeline):
         # Create PerfData structures for computing cross-validation metrics
         em = perf.EpochManagerKFold(self,
                                 subsets={'train':'train_valid', 'valid':'valid', 'test':'test'},
-                                prediction_type=self.params.prediction_type, 
-                                model_dataset=pipeline.data, 
+                                prediction_type=self.params.prediction_type,
+                                model_dataset=pipeline.data,
                                 production=self.params.production)
 
         em.on_new_best_valid(lambda : 1+1) # does not need to take any action
@@ -967,7 +1001,7 @@ def train_kfold_cv(self, pipeline):
 
                 train_perf = train_perf_data.accumulate_preds(train_pred, train_dset.ids)
                 test_perf = test_perf_data.accumulate_preds(test_pred, test_dset.ids)
-                
+
                 # update the make pred function to include latest transformers
                 def make_pred(x):
                     return self.model.predict(x, self.transformers[k])
@@ -1011,13 +1045,13 @@ def make_pred(x):
         # Only copy the model files we need, not the entire directory
         self._copy_model(self.best_model_dir)
         retrain_time = time.time() - retrain_start
-        self.log.info("Time to retrain model for %d epochs: %.1f seconds, %.1f sec/epoch" % (self.best_epoch, retrain_time, 
+        self.log.info("Time to retrain model for %d epochs: %.1f seconds, %.1f sec/epoch" % (self.best_epoch, retrain_time,
                        retrain_time/max(1, self.best_epoch)))
 
     # ****************************************************************************************
     def train_with_early_stopping(self, pipeline):
         """Training method for neural networks without k-fold cross validation that allows
-        early stopping when validation metric fails to improve for specified number of epochs. 
+        early stopping when validation metric fails to improve for specified number of epochs.
 
         Trains a neural net model for up to self.params.max_epochs epochs, while tracking the validation
         set metric given by params.model_choice_score_type. Saves a model checkpoint each time the metric
@@ -1049,8 +1083,8 @@ def train_with_early_stopping(self, pipeline):
         self.data = pipeline.data
 
         em = perf.EpochManager(self,
-                                prediction_type=self.params.prediction_type, 
-                                model_dataset=pipeline.data, 
+                                prediction_type=self.params.prediction_type,
+                                model_dataset=pipeline.data,
                                 production=self.params.production)
         def make_pred(dset):
             return self.model.predict(dset, self.transformers['final'])
@@ -1134,9 +1168,9 @@ def generate_predictions(self, dataset):
         # Current (2.1) DeepChem neural net classification models don't support uncertainties.
         if self.params.uncertainty and self.params.prediction_type == 'classification':
             self.log.warning("Warning: DeepChem neural net models support uncertainty for regression only.")
- 
+
         if self.params.uncertainty and self.params.prediction_type == 'regression':
-            # For the models we use, predict_uncertainty returns a tuple (not a list of tuples) for both singletask and multitask. 
+            # For the models we use, predict_uncertainty returns a tuple (not a list of tuples) for both singletask and multitask.
             # A list is only returned if we request multiple *outputs* (e.g., predictions and embeddings), which are not the same thing as tasks.
 
             # Fully connected NN models return predictions and uncertainties as arrays with shape (num_cmpds, num_tasks, num_classes), with
@@ -1177,7 +1211,7 @@ def generate_predictions(self, dataset):
 
                 # =-=ksm: The second 'isinstance' shouldn't be necessary since NormalizationTransformerMissingData
                 # is a subclass of dc.trans.NormalizationTransformer.
-                if len(self.transformers) == 1 and (isinstance(self.transformers['final'][0], dc.trans.NormalizationTransformer) 
+                if len(self.transformers) == 1 and (isinstance(self.transformers['final'][0], dc.trans.NormalizationTransformer)
                                                  or isinstance(self.transformers['final'][0],trans.NormalizationTransformerMissingData)):
                     y_stds = self.transformers['final'][0].y_stds.reshape((1,ntasks,1))
                     std = std / y_stds
@@ -1185,7 +1219,7 @@ def generate_predictions(self, dataset):
         else:
             # Classification models and regression models without uncertainty are handled here
             if (not self.params.transformers or self.transformers is None):
-                txform = [] 
+                txform = []
             else:
                 txform = self.transformers['final']
 
@@ -1294,15 +1328,15 @@ def __init__(self, params, featurizer, ds_client, random_state=None, seed=None):
                 ("dp1", torch.nn.Dropout(p=self.params.dropouts[0]).to(self.dev)),
                 ("relu1", torch.nn.ReLU().to(self.dev))
             ])
-            
+
             if len(self.params.layer_sizes) > 1:
                 for i in range(1, len(self.params.layer_sizes)):
                     model_dict[f"layer{i+1}"] = torch.nn.Linear(self.params.layer_sizes[i-1], self.params.layer_sizes[i]).to(self.dev)
                     model_dict[f"dp{i+1}"] = torch.nn.Dropout(p=self.params.dropouts[i]).to(self.dev)
                     model_dict[f"relu{i+1}"] = torch.nn.ReLU().to(self.dev)
-            
+
             model_dict["last_layer"] = torch.nn.Linear(self.params.layer_sizes[-1], 1).to(self.dev)
-            
+
             self.model_dict = model_dict
             self.model = torch.nn.Sequential(model_dict).to(self.dev)
         else:
@@ -1314,7 +1348,7 @@ def _predict_binding(self, activity, conc):
         is needed. It can be the ratio of concentration and Kd of the radioligand in a competitive binding assay, or the concentration
         of the substrate and Michaelis constant (Km) of enzymatic inhibition assay.
         """
-        
+
         if self.params.is_ki:
             if self.params.ki_convert_ratio is None:
                 raise Exception("Ki converting ratio is missing. Cannot convert Ki into IC50")
@@ -1323,7 +1357,7 @@ def _predict_binding(self, activity, conc):
         else:
             IC50 = 10**(9-activity)
         pred_frac = 1.0/(1.0 + IC50/conc)
-        
+
         return pred_frac
 
     def _l2_loss(self, yp, yr):
@@ -1391,10 +1425,10 @@ def _loss_batch(self, loss_func, xb, yb, opt=None):
 
         loss_ki, loss_bind = self.loss_func(self.model(xb), yb)
         loss = loss_ki + loss_bind
-        
+
         if opt is not None:
             loss.backward()
-            opt.step()   
+            opt.step()
             opt.zero_grad()
 
         return loss_ki.item(), loss_bind.item(), len(xb)
@@ -1406,7 +1440,7 @@ def __init__(self, ds, dl, n_ki, n_bind):
             self.dl = dl
             self.n_ki = n_ki
             self.n_bind = n_bind
-    
+
     def _tensorize(self, x):
             return torch.tensor(x, dtype=torch.float32)
 
@@ -1425,23 +1459,23 @@ def _load_hybrid_data(self, data):
             # train
             train_ki_pos = np.where(np.isnan(y_train[:,1].numpy()))[0]
             train_bind_pos = np.where(~np.isnan(y_train[:,1].numpy()))[0]
-            
+
             # valid
             valid_ki_pos = np.where(np.isnan(y_valid[:,1].numpy()))[0]
             valid_bind_pos = np.where(~np.isnan(y_valid[:,1].numpy()))[0]
-            
+
             train_ds = TensorDataset(x_train, y_train)
             train_dl = DataLoader(train_ds, batch_size=self.params.batch_size, shuffle=True, pin_memory=True)
-            train_data = self.SubsetData(train_ds, 
-                                        train_dl, 
-                                        len(train_ki_pos), 
+            train_data = self.SubsetData(train_ds,
+                                        train_dl,
+                                        len(train_ki_pos),
                                         len(train_bind_pos))
 
             valid_ds = TensorDataset(x_valid, y_valid)
             valid_dl = DataLoader(valid_ds, batch_size=self.params.batch_size * 2, pin_memory=True)
-            valid_data = self.SubsetData(valid_ds, 
-                                        valid_dl, 
-                                        len(valid_ki_pos), 
+            valid_data = self.SubsetData(valid_ds,
+                                        valid_dl,
+                                        len(valid_ki_pos),
                                         len(valid_bind_pos))
 
             self.train_valid_dsets.append((train_data, valid_data))
@@ -1454,13 +1488,13 @@ def _load_hybrid_data(self, data):
 
         test_ds = TensorDataset(x_test, y_test)
         test_dl = DataLoader(test_ds, batch_size=self.params.batch_size * 2, pin_memory=True)
-        test_data = self.SubsetData(test_ds, 
-                                    test_dl, 
-                                    len(test_ki_pos), 
+        test_data = self.SubsetData(test_ds,
+                                    test_dl,
+                                    len(test_ki_pos),
                                     len(test_bind_pos))
 
         self.test_data = test_data
-    
+
     def save_model(self, checkpoint_file, model, opt, epoch, model_dict):
         """Save a model to a checkpoint file.
         Include epoch, model_dict in checkpoint dict.
@@ -1471,7 +1505,7 @@ def save_model(self, checkpoint_file, model, opt, epoch, model_dict):
             opt_state_dict=opt.state_dict(),
             model_dict=model_dict
             )
-        
+
         torch.save(checkpoint, checkpoint_file)
 
     def train(self, pipeline):
@@ -1483,7 +1517,7 @@ def train(self, pipeline):
         # load hybrid data
         self._load_hybrid_data(pipeline.data)
 
-        checkpoint_file = os.path.join(self.model_dir, 
+        checkpoint_file = os.path.join(self.model_dir,
             f"{self.params.dataset_name}_model_{self.params.model_uuid}.pt")
 
         opt = torch.optim.Adam(self.model.parameters(), lr=self.params.learning_rate)
@@ -1491,7 +1525,6 @@ def train(self, pipeline):
         em = perf.EpochManager(self,
                                 prediction_type="hybrid",
                                 model_dataset=pipeline.data,
-                                transformers=self.transformers,
                                 is_ki=self.params.is_ki,
                                 production=self.params.production,
                                 ki_convert_ratio=self.params.ki_convert_ratio)
@@ -1499,7 +1532,7 @@ def train(self, pipeline):
         em.set_make_pred(lambda x: self.generate_predictions(x)[0])
         # initialize ei here so we can use it in the closure
         ei = 0
-        em.on_new_best_valid(lambda : self.save_model(checkpoint_file, self.model, 
+        em.on_new_best_valid(lambda : self.save_model(checkpoint_file, self.model,
             opt, ei, self.model_dict))
 
         train_dset, valid_dset = pipeline.data.train_valid_dsets[0]
@@ -1532,7 +1565,7 @@ def train(self, pipeline):
                 valid_loss_ep /= (valid_data.n_ki + valid_data.n_bind)
 
             train_perf, valid_perf, test_perf = em.update_epoch(ei,
-                                train_dset=train_dset, valid_dset=valid_dset, test_dset=test_dset, fold='final')
+                                train_dset=train_dset, valid_dset=valid_dset, test_dset=test_dset)
 
             self.log.info("Epoch %d: training %s = %.3f, training loss = %.3f, validation %s = %.3f, validation loss = %.3f, test %s = %.3f" % (
                           ei, pipeline.metric_type, train_perf, train_loss_ep, pipeline.metric_type, valid_perf, valid_loss_ep,
@@ -1542,7 +1575,7 @@ def train(self, pipeline):
             self.num_epochs_trained = ei + 1
             if em.should_stop():
                 break
- 
+
         # Revert to last checkpoint
         checkpoint = torch.load(checkpoint_file)
         self.model.load_state_dict(checkpoint['model_state_dict'])
@@ -1564,7 +1597,7 @@ def reload_model(self, reload_dir):
         Side effects:
             Resets the value of model, transformers, and transformers_x
         """
-        
+
         checkpoint_file = os.path.join(reload_dir, f"{self.params.dataset_name}_model_{self.params.model_uuid}.pt")
         if os.path.isfile(checkpoint_file):
             checkpoint = torch.load(checkpoint_file)
@@ -1574,7 +1607,7 @@ def reload_model(self, reload_dir):
             self.model.eval()
         else:
             raise Exception(f"Checkpoint file doesn't exist in the reload_dir {reload_dir}")
-        
+
         # Restore the transformers from the datastore or filesystem
         self.reload_transformers()
 
@@ -1603,9 +1636,9 @@ def generate_predictions(self, dataset):
 
         data_ds = TensorDataset(x_data, y_data)
         data_dl = DataLoader(data_ds, batch_size=self.params.batch_size * 2, pin_memory=True)
-        _data_data = self.SubsetData(data_ds, 
-                                    data_dl, 
-                                    len(data_ki_pos), 
+        _data_data = self.SubsetData(data_ds,
+                                    data_dl,
+                                    len(data_ki_pos),
                                     len(data_bind_pos))
         pred = []
         real = []
@@ -2018,7 +2051,7 @@ def get_model_specific_metadata(self):
         }
         model_spec_metadata = dict(rf_specific = rf_metadata)
         return model_spec_metadata
-    
+
 # ****************************************************************************************
 class DCxgboostModelWrapper(ForestModelWrapper):
     """ModelWrapper class for gradient-boosted tree models, as implemented in the xgboost package.
@@ -2093,8 +2126,7 @@ def make_dc_model(self, model_dir):
                                          missing=np.nan,
                                          importance_type='gain',
                                          n_jobs=-1,
-                                         gpu_id = -1,
-                                         n_gpus = 0,
+                                         device='cpu',
                                          max_bin = 16,
                                          )
         else:
@@ -2127,9 +2159,8 @@ def make_dc_model(self, model_dir):
                                           random_state=self.seed,
                                           importance_type='gain',
                                           missing=np.nan,
-                                          gpu_id = -1,
-                                          n_jobs=-1,                                          
-                                          n_gpus = 0,
+                                          device='cpu',
+                                          n_jobs=-1,
                                           max_bin = 16,
                                          )
 
@@ -2241,8 +2272,7 @@ def reload_model(self, reload_dir):
                                          missing=np.nan,
                                          importance_type='gain',
                                          n_jobs=-1,
-                                         gpu_id = -1,
-                                         n_gpus = 0,
+                                         device='cpu',
                                          max_bin = 16,
                                          )
         else:
@@ -2262,12 +2292,11 @@ def reload_model(self, reload_dir):
                                          reg_lambda=self.params.xgb_lambda,
                                          scale_pos_weight=1,
                                          base_score=0.5,
-                                         random_state=self.seed, 
+                                         random_state=self.seed,
                                          importance_type='gain',
                                          missing=np.nan,
-                                         gpu_id = -1,
-                                         n_jobs=-1,                                          
-                                         n_gpus = 0,
+                                         device='cpu',
+                                         n_jobs=-1,
                                          max_bin = 16,
                                          )
 
@@ -2422,7 +2451,7 @@ def __init__(self, params, featurizer, ds_client, random_state=None, seed=None):
             featurizer (Featurization): Object managing the featurization of compounds
             ds_client: datastore client.
         """
-        # use NNModelWrapper init. 
+        # use NNModelWrapper init.
         super().__init__(params, featurizer, ds_client, random_state=random_state, seed=seed)
         self.num_epochs_trained = 0
         self.model = self.recreate_model()
@@ -2452,7 +2481,7 @@ def recreate_model(self, **kwargs):
         model = chosen_model(
                 sed = self.seed,
                 **extracted_features
-            ) 
+            )
 
         return model
 
@@ -2563,7 +2592,7 @@ def train_with_early_stopping(self, pipeline):
         early stopping when validation metric fails to improve for specified number of epochs. Differs from
         superclass NNModelWrapper implementation by saving mean input feature weights by epoch, providing a
         way to monitor effects of different weight_decay_penalty settings.
-        
+
         Trains a neural net model for up to self.params.max_epochs epochs, while tracking the validation
         set metric given by params.model_choice_score_type. Saves a model checkpoint each time the metric
         is improved over its previous saved value by more than a threshold percentage. If the metric fails to
@@ -2593,12 +2622,11 @@ def train_with_early_stopping(self, pipeline):
         """
         self.data = pipeline.data
         feature_names = self.data.featurization.get_feature_columns()
-        nfeatures = len(feature_names)
         self.feature_weights = dict(zip(feature_names, [[] for f in feature_names]))
 
         em = perf.EpochManager(self,
-                                prediction_type=self.params.prediction_type, 
-                                model_dataset=pipeline.data, 
+                                prediction_type=self.params.prediction_type,
+                                model_dataset=pipeline.data,
                                 production=self.params.production)
         def make_pred(dset):
             return self.model.predict(dset, self.transformers['final'])
@@ -2621,10 +2649,8 @@ def make_pred(dset):
                           pipeline.metric_type, test_perf))
 
             layer1_weights = self.model.model.layers[0].weight
-            feature_weights = np.zeros(nfeatures, dtype=float)
-            for node_weights in layer1_weights:
-                node_feat_weights = torch.abs(node_weights).detach().numpy()
-                feature_weights += node_feat_weights
+            feature_weights = layer1_weights.detach().abs().mean(dim=0).cpu().numpy()
+
             for fnum, fname in enumerate(feature_names):
                 self.feature_weights[fname].append(feature_weights[fnum])
 
@@ -2742,7 +2768,7 @@ def get_model_specific_metadata(self):
             model_spec_metdata (dict): A dictionary of the parameter sets for the MultitaskDCModelWrapper object.
                 Parameters are saved under the key 'nn_specific' as a subdictionary.
         """
-        
+
         nn_metadata = dict(
                     best_epoch = self.best_epoch,
                     max_epochs = self.params.max_epochs,
@@ -2775,7 +2801,7 @@ def _copy_model(self, dest_dir):
 
         chkpt_file = os.path.join(self.model_dir, 'checkpoint')
         with open(chkpt_file, 'r') as chkpt_in:
-            chkpt_dict = yaml.load(chkpt_in.read())
+            chkpt_dict = yaml.safe_load(chkpt_in)
         chkpt_prefix = chkpt_dict['model_checkpoint_path']
         files = [chkpt_file]
         # files.append(os.path.join(self.model_dir, 'model.pickle'))
@@ -2831,7 +2857,7 @@ def count_params(self):
 class GraphConvDCModelWrapper(KerasDeepChemModelWrapper):
     """ModelWrapper subclass for Duvenaud style graph convolution models, as implemented in the DeepChem
     GraphConvModel model class.
-    
+
     Contains methods to load in a dataset, split and featurize the data, fit a model to the train dataset,
     generate predictions for an input dataset, and generate performance metrics for these predictions.
 
@@ -2988,4 +3014,4 @@ def get_model_specific_metadata(self):
                     weight_decay_penalty_type=self.params.weight_decay_penalty_type
         )
         model_spec_metadata = dict(nn_specific = nn_metadata)
-        return model_spec_metadata
\ No newline at end of file
+        return model_spec_metadata
diff --git a/atomsci/ddm/pipeline/parameter_parser.py b/atomsci/ddm/pipeline/parameter_parser.py
index b1a6cdff..a4183ee7 100644
--- a/atomsci/ddm/pipeline/parameter_parser.py
+++ b/atomsci/ddm/pipeline/parameter_parser.py
@@ -7,6 +7,7 @@
 
 import deepchem.models as dcm
 import deepchem.models.torch_models as dcmt
+from deepchem.models import GraphConvModel
 import deepchem.feat as dcf
 import inspect
 
@@ -32,10 +33,10 @@
                       'model_dir':'result_dir',
                     }
 
-model_wl = {'AttentiveFPModel':dcm.AttentiveFPModel, 
+model_wl = {'AttentiveFPModel':dcm.AttentiveFPModel,
             'GCNModel':dcm.GCNModel,
-            'MPNNModel':dcm.MPNNModel,
-            'GraphConvModel':dcm.GraphConvModel,
+            'MPNNModel':dcmt.MPNNModel,
+            'GraphConvModel': GraphConvModel,
             'PytorchMPNNModel':dcmt.MPNNModel}#, dcm.GCNModel, dcm.GATModel]
 
 # featurizer white list
@@ -139,7 +140,7 @@ def all_auto_lists():
 def extract_model_params(params, strip_prefix=True):
     """Extract parameters specific to a model.
 
-    This function extracts parameters intended for a specific model. It should only be used 
+    This function extracts parameters intended for a specific model. It should only be used
     for arguments automatically added by an AutoArgumentAdder.
 
     Args:
@@ -148,7 +149,7 @@ def extract_model_params(params, strip_prefix=True):
             For example, 'AttentiveFP_mode' becomes 'mode'.
 
     Returns:
-        dict: A dictionary containing a subset of parameters from `params` that are relevant 
+        dict: A dictionary containing a subset of parameters from `params` that are relevant
             to the specified model.
     """
     assert params.model_type in model_wl
@@ -159,7 +160,7 @@ def extract_model_params(params, strip_prefix=True):
 def extract_featurizer_params(params, strip_prefix=True):
     """Extract parameters specific to a featurizer.
 
-    This function extracts parameters intended for a specific featurizer. It should only be used 
+    This function extracts parameters intended for a specific featurizer. It should only be used
     for arguments automatically added by an AutoArgumentAdder.
 
     Args:
@@ -168,7 +169,7 @@ def extract_featurizer_params(params, strip_prefix=True):
             For example, 'MolGraphConvFeaturizer_use_edges' becomes 'use_edges'.
 
     Returns:
-        dict: A dictionary containing a subset of parameters from `params` that are relevant 
+        dict: A dictionary containing a subset of parameters from `params` that are relevant
             to the specified featurizer.
     """
     assert params.featurizer in featurizer_wl
@@ -298,7 +299,7 @@ def is_list(p, type_annotation):
         return str(type_annotation).startswith('typing.List') or str(type_annotation) == "<class 'list'>"
 
 def strip_optional(type_annotation):
-    """In the upgrade to python 3.9 type_annotaions now use
+    """In the upgrade to python 3.10 type_annotaions now use
         typeing.Optional and we need to strip that off.
 
     Args:
@@ -530,7 +531,7 @@ def get_list_args(self):
 # Parameters that may take lists of values, usually but not always in the context of a hyperparam search
 
 convert_to_float_list = {'dropouts','weight_init_stddevs','bias_init_consts','learning_rate',
-                         'umap_targ_wt', 'umap_min_dist', 'dropout_list','weight_decay_penalty',
+                         'dropout_list','weight_decay_penalty',
                          'xgb_learning_rate',
                          'xgb_gamma',
                          'xgb_alpha',
@@ -538,10 +539,11 @@ def get_list_args(self):
                          "xgb_min_child_weight",
                          "xgb_subsample",
                          "xgb_colsample_bytree",
-                         "ki_convert_ratio"
+                         "ki_convert_ratio",
+                         "robustscaler_quartile_range"
                          }
 convert_to_int_list = {'layer_sizes','rf_max_features','rf_estimators', 'rf_max_depth',
-                       'umap_dim', 'umap_neighbors', 'layer_nums', 'node_nums',
+                       'layer_nums', 'node_nums',
                        'xgb_max_depth',  'xgb_n_estimators', 'seed'}.union(all_auto_int_lists())
 convert_to_numeric_list = convert_to_float_list | convert_to_int_list
 keep_as_list = {'dropouts','weight_init_stddevs','bias_init_consts',
@@ -557,9 +559,9 @@ def get_list_args(self):
                                      'xgb_max_depth',  'xgb_n_estimators'
                                      }
 convert_to_str_list = \
-    {'response_cols','model_type','featurizer','splitter','umap_metric','weight_decay_penalty_type','descriptor_type'}
+    {'response_cols','model_type','featurizer','splitter','weight_decay_penalty_type','descriptor_type'}
 not_a_str_list_outside_of_hyperparams = \
-    {'model_type','featurizer','splitter','umap_metric','weight_decay_penalty_type','descriptor_type'}
+    {'model_type','featurizer','splitter','weight_decay_penalty_type','descriptor_type'}
 
 #**********************************************************************************************************
 def to_str(params_obj):
@@ -781,8 +783,14 @@ def dict_to_list(inp_dictionary,replace_spaces=False):
     temp_list_to_command_line = []
 
     # Special case handling for arguments that are False or True by default
-    default_false = ['previously_split','use_shortlist','datastore', 'save_results','verbose', 'hyperparam', 'split_only', 'is_ki', 'production', 'embedding_and_features'] 
-    default_true = ['transformers','previously_featurized','uncertainty', 'rerun']
+    default_false = ['previously_split','use_shortlist','datastore', 
+                    'save_results','verbose', 'hyperparam', 'split_only', 'is_ki', 'production', 
+                    'embedding_and_features', 
+                    'robustscaler_unit_variance']
+    default_true = ['transformers','previously_featurized','uncertainty', 'rerun',
+                    'robustscaler_with_centering', 'robustscaler_with_scaling',
+                    'powertransformer_standardize']
+
     for key, value in inp_dictionary.items():
         if key in default_false:
             true_options = ['True','true','ture','TRUE','Ture']
@@ -1026,6 +1034,10 @@ def get_parser():
         '--model_choice_score_type', dest='model_choice_score_type', required=False, default=None,
         help='Type of score function used to choose best epoch and/or hyperparameters (defaults to "roc_auc" '
              'for classification and "r2" for regression). ')
+    parser.add_argument(
+        '--max_invalid_pred_frac', dest='max_invalid_pred_frac', required=False, type=float, default=0.01,
+        help='Maximum fraction of NaN/Inf predictions to filter before applying hard metric penalties. '
+             'Must be between 0.0 and 1.0.')
     parser.add_argument(
         '--model_type', dest='model_type', default=None, type=str,
         help='Type of model to fit (NN, RF, or xgboost). The model_type sets the model subclass in model_wrapper. '
@@ -1244,7 +1256,7 @@ def get_parser():
     parser.add_argument(
         '--sampling_method', dest='sampling_method', type=str, default=None,
         help='Method for sampling to address class imbalance (e.g., \'undersampling\', \'SMOTE\')')
-    
+
     parser.add_argument(
         '--sampling_ratio', dest='sampling_ratio', type=str, default='auto',
         help='The "sampling_ratio" parameter of SMOTE must be a float in the range (0.0, 1.0], a str '
@@ -1281,11 +1293,13 @@ def get_parser():
     # **********************************************************************************************************
     # model_building_parameters: transformers
     parser.add_argument(
-        '--feature_transform_type', dest='feature_transform_type', choices=['normalization', 'umap'],
+        '--feature_transform_type', dest='feature_transform_type', 
+        choices=['normalization', 'RobustScaler', 'PowerTransformer', 'Identity'],
         default='normalization', help='type of transformation for the features')
     parser.add_argument(
         '--response_transform_type', dest='response_transform_type', default='normalization',
-        help='type of normalization for the response column TODO: Not currently implemented')
+        choices=['normalization'],
+        help='type of normalization for the response column.')
     parser.add_argument(
         '--weight_transform_type', dest='weight_transform_type', choices=[None, 'None', 'balancing'], default=None,
         help='type of normalization for the weights')
@@ -1305,28 +1319,59 @@ def get_parser():
         help='Boolean switch for using transformation on regression output. Default is True')
     parser.set_defaults(transformers=True)
 
-    # **********************************************************************************************************
-    # model_building_parameters: UMAP
-    parser.add_argument(
-        '--umap_dim', dest='umap_dim', required=False, default='10',
-        help='Dimension of projected feature space, if UMAP transformation is requested. Can be input as a comma '
-             'separated list for hyperparameter search (e.g. \'2,6,10\').')
-    parser.add_argument(
-        '--umap_metric', dest='umap_metric', required=False, default='euclidean',
-        help='Distance metric used, if UMAP transformation is requested. Can be input as a comma separated list '
-             'for hyperparameter search (e.g. \'euclidean\',\'cityblock\')')
+    # Load transformer
     parser.add_argument(
-        '--umap_min_dist', dest='umap_min_dist', required=False, default='0.05',
-        help='Minimum distance used in UMAP projection, if UMAP transformation is requested. Can be input as a '
-             'comma separated list for hyperparameter search (e.g. \'0.01,0.02,0.05\')')
-    parser.add_argument(
-        '--umap_neighbors', dest='umap_neighbors', required=False, default='20',
-        help='Number of nearest neighbors used in UMAP projection, if UMAP transformation is requested. Can be input '
-             'as a comma separated list for hyperparameter search (e.g. \'10,20,30\')')
-    parser.add_argument(
-        '--umap_targ_wt', dest='umap_targ_wt', required=False, default='0.0',
-        help='Weight given to training set response values in UMAP projection, if UMAP transformation is requested.'
-             ' Can be input as a comma separated list for hyperparameter search (e.g. \'0.0,0.1,0.2\')')
+        '--feature_transform_path', dest='feature_transform_path',
+        type=str, default=None, help='Path to a transformer pkl created using generate_transformers. This '
+        'will overwrite any relevant transformer parameters with values loaded from the pkl'
+    )
+
+    # RobustScaler parameters
+    parser.add_argument(
+        '--robustscaler_with_centering', action='store_false',
+        help='If True, center the data before scaling. '
+        'This will cause transform to raise an exception when attempted on sparse matrices, '
+        'because centering them entails building a dense matrix which in common use '
+        'cases is likely to be too large to fit in memory. Default is True')
+    parser.set_defaults(robustscaler_with_centering=True)
+    parser.add_argument(
+        '--robustscaler_with_scaling', action='store_false',
+        help='If True, scale the data to interquartile range. Default is True')
+    parser.set_defaults(robustscaler_with_scaling=True)
+    parser.add_argument(
+        '--robustscaler_quartile_range', type=str, default='25.0,75.0',
+        help='Quantile range used to calculate scale_. '
+        'By default this is equal to the IQR, i.e., '
+        'q_min is the first quantile and q_max is the third quantile. '
+        '(q_min, q_max), 0.0 < q_min < q_max < 100.0. Default is "25.0,75.0"')
+    parser.add_argument(
+        '--robustscaler_unit_variance', action='store_true',
+        help='If True, scale data so that normally distributed features have a variance of 1. '
+        'In general, if the difference between the x-values of q_max and q_min for a standard '
+        'normal distribution is greater than 1, the dataset will be scaled down. '
+        'If less than 1, the dataset will be scaled up. Default is False.')
+    parser.set_defaults(robustscaler_unit_variance=False)
+
+    # PowerTransformer parameters
+    parser.add_argument(
+        '--powertransformer_method', choices=['yeo-johnson', 'box-cox'],
+        default='yeo-johnson',
+        help='The power transform method. Available methods are: "yeo-johnson", '
+        'works with positive and negative values "box-cox", only works with strictly positive values. '
+        'Choices are {"yeo-johnson", "box-cox"}')
+    parser.add_argument(
+        '--powertransformer_standardize', action='store_false',
+        help='Set to True to apply zero-mean, unit-variance normalization to the transformed output. '
+        'Default is True.')
+    parser.set_defaults(powertransformer_standardize=True)
+
+    # Sklearn parameters
+    parser.add_argument(
+        '--imputer_strategy', choices=['mean', 'median', 'most_frequent'],
+        default='mean',
+        help='This sets the imputer strategy for the SimpleImputer for use with'
+        'PowerTransformer or RobustScaler.'
+        'Choices are {"mean", "median", "most_frequent"}')
 
     # **********************************************************************************************************
     # model_building_parameters: XGBoost
@@ -1470,9 +1515,9 @@ def get_parser():
             help='Scaling factor for constraining network size based on number of parameters in the network for '
                  'hyperparam search')
     parser.add_argument(
-        '--python_path', dest='python_path', required=False, 
+        '--python_path', dest='python_path', required=False,
         # default to the version of python used to run this script
-        default=sys.executable, 
+        default=sys.executable,
         help='Path to desired python version')
     parser.add_argument(
             '--rerun', dest= 'rerun', required=False, action='store_false',
@@ -1480,14 +1525,14 @@ def get_parser():
                  'already been built')
     parser.set_defaults(rerun=True)
     parser.add_argument(
-        '--script_dir', dest='script_dir', required=False, 
+        '--script_dir', dest='script_dir', required=False,
         # use location of this file to generate script dir
         default=os.path.abspath(os.path.join(__file__, '../..')),
         help='Path where pipeline file you want to run hyperparam search from is located')
 
     parser.add_argument(
         '--search_type', dest='search_type', required=False, default='grid',
-        help='Type of hyperparameter search to do. Options = [grid, random, geometric, and user_specified]')
+        help='Type of hyperparameter search to do. Options = [grid, random, geometric, user_specified, and hyperopt (Optuna Bayesian search)]')
 
     parser.add_argument(
         '--split_only', dest='split_only', required=False, action='store_true',
@@ -1528,64 +1573,64 @@ def get_parser():
         help='Time limit in minutes for hyperparameter search batch jobs.'
              'If set to None then this parameter will not be used.')
 
-    # HyperOptSearch specific parameters
+    # OptunaSearch specific parameters
     # NN model
     parser.add_argument(
         '--lr', dest='lr', required=False, default=None,
-        help='learning rate shown in HyperOpt domain format, e.g. --lr=uniform|0.00001,0.001')
+        help='learning rate shown in Optuna domain format, e.g. --lr=uniform|0.00001,0.001')
     parser.add_argument(
         '--ls', dest='ls', required=False, default=None,
-        help='layer sizes shown in HyperOpt domain format, e.g. --ls=choice|2|8,16,32,64,128,256,512')
+        help='layer sizes shown in Optuna domain format, e.g. --ls=choice|2|8,16,32,64,128,256,512')
     parser.add_argument(
         '--ls_ratio', dest='ls_ratio', required=False, default=None,
-        help='layer size ratios (layer size / previous layer size) shown in HyperOpt domain format, the number of layers is not needed here, taken from ls, e.g. --ls_ratio=uniform|0.1,0.9')
+        help='layer size ratios (layer size / previous layer size) shown in Optuna domain format, the number of layers is not needed here, taken from ls, e.g. --ls_ratio=uniform|0.1,0.9')
     parser.add_argument(
         '--dp', dest='dp', required=False, default=None,
-        help='dropouts shown in HyperOpt domain format, e.g. --dp=uniform|3|0,0.4')
+        help='dropouts shown in Optuna domain format, e.g. --dp=uniform|3|0,0.4')
     parser.add_argument(
         '--wdp', dest='wdp', required=False, default=None,
-        help='weight_decay_penalty shown in HyperOpt domain format, e.g. --wdp=loguniform|-6.908,-4.605')
+        help='weight_decay_penalty shown in Optuna domain format, e.g. --wdp=loguniform|-6.908,-4.605')
     parser.add_argument(
         '--wdt', dest='wdt', required=False, default=None,
-        help='weight_decay_penalty_type shown in HyperOpt domain format, e.g. --wdt=choice|l1,l2')
+        help='weight_decay_penalty_type shown in Optuna domain format, e.g. --wdt=choice|l1,l2')
     # RF model
     parser.add_argument(
         '--rfe', dest='rfe', required=False, default=None,
-        help='rf_estimators shown in HyperOpt domain format, e.g. --rfe=uniformint|64,512')
+        help='rf_estimators shown in Optuna domain format, e.g. --rfe=uniformint|64,512')
     parser.add_argument(
         '--rfd', dest='rfd', required=False, default=None,
-        help='rf_max_depth shown in HyperOpt domain format, e.g. --rfd=uniformint|64,512')
+        help='rf_max_depth shown in Optuna domain format, e.g. --rfd=uniformint|64,512')
     parser.add_argument(
         '--rff', dest='rff', required=False, default=None,
-        help='rf_max_features shown in HyperOpt domain format, e.g. --rff=uniformint|64,512')
+        help='rf_max_features shown in Optuna domain format, e.g. --rff=uniformint|64,512')
     # XGBoost model
     parser.add_argument(
         '--xgbg', dest='xgbg', required=False, default=None,
-        help='xgb_gamma shown in HyperOpt domain format, e.g. --xgbg=uniform|0,0.4')
+        help='xgb_gamma shown in Optuna domain format, e.g. --xgbg=uniform|0,0.4')
     parser.add_argument(
         '--xgba', dest='xgba', required=False, default=None,
-        help='xgb_alpha shown in HyperOpt domain format, e.g. --xgba=uniform|0,0.4')
+        help='xgb_alpha shown in Optuna domain format, e.g. --xgba=uniform|0,0.4')
     parser.add_argument(
         '--xgbb', dest='xgbb', required=False, default=None,
-        help='xgb_lambda shown in HyperOpt domain format, e.g. --xgbb=uniform|0,0.4')
+        help='xgb_lambda shown in Optuna domain format, e.g. --xgbb=uniform|0,0.4')
     parser.add_argument(
         '--xgbl', dest='xgbl', required=False, default=None,
-        help='xgb_learning_rate shown in HyperOpt domain format, e.g. --xgbl=loguniform|-6.9,-2.3')
+        help='xgb_learning_rate shown in Optuna domain format, e.g. --xgbl=loguniform|-6.9,-2.3')
     parser.add_argument(
         '--xgbd', dest='xgbd', required=False, default=None,
-        help='xgb_max_depth shown in HyperOpt domain format, e.g. --xgbd=uniformint|3,10')
+        help='xgb_max_depth shown in Optuna domain format, e.g. --xgbd=uniformint|3,10')
     parser.add_argument(
         '--xgbc', dest='xgbc', required=False, default=None,
-        help='xgb_colsample_bytree shown in HyperOpt domain format, e.g. --xgbc=uniform|0.1,1.0')
+        help='xgb_colsample_bytree shown in Optuna domain format, e.g. --xgbc=uniform|0.1,1.0')
     parser.add_argument(
         '--xgbs', dest='xgbs', required=False, default=None,
-        help='xgb_subsample shown in HyperOpt domain format, e.g. --xgbs=uniform|0.1,1.0')
+        help='xgb_subsample shown in Optuna domain format, e.g. --xgbs=uniform|0.1,1.0')
     parser.add_argument(
         '--xgbn', dest='xgbn', required=False, default=None,
-        help='xgb_n_estimators shown in HyperOpt domain format, e.g. --xgbn=choice|200,500,1000')
+        help='xgb_n_estimators shown in Optuna domain format, e.g. --xgbn=choice|200,500,1000')
     parser.add_argument(
         '--xgbw', dest='xgbw', required=False, default=None,
-        help='xgb_min_child_weight shown in HyperOpt domain format, e.g. --xgbw=uniform|1.0,1.2')
+        help='xgb_min_child_weight shown in Optuna domain format, e.g. --xgbw=uniform|1.0,1.2')
     # checkpoint
     parser.add_argument(
         '--hp_checkpoint_save', dest='hp_checkpoint_save', required=False, default=None,
@@ -1686,6 +1731,11 @@ def postprocess_args(parsed_args):
         else:
             parsed_args.model_choice_score_type = 'r2'
 
+    if parsed_args.max_invalid_pred_frac is None:
+        parsed_args.max_invalid_pred_frac = 0.01
+    if parsed_args.max_invalid_pred_frac < 0.0 or parsed_args.max_invalid_pred_frac > 1.0:
+        raise Exception("max_invalid_pred_frac must be between 0.0 and 1.0.")
+
     # Convert arguments passed as comma-separated values into lists
     if parsed_args.hyperparam:
         for item in convert_to_str_list:
@@ -1788,7 +1838,7 @@ def postprocess_args(parsed_args):
 #***********************************************************************************************************
 def validate_sampling_strategy_argument(value):
     """Validates sampling_strategy parameter for SMOTE and undersampling.
-    Validates that the input value is either a float in the range (0.0, 1.0] or a string among 
+    Validates that the input value is either a float in the range (0.0, 1.0] or a string among
     {'auto', 'not majority', 'minority', 'all', 'not minority'}. Raises a ValueError if the validation fails.
 
     Args:
@@ -1798,7 +1848,7 @@ def validate_sampling_strategy_argument(value):
         ValueError: If the value is not a float in the range (0.0, 1.0] or a valid string.
     """
     valid_strings = {"auto", "not majority", "minority", "all", "not minority"}
-    
+
     try:
         float_value = float(value)
         if float_value <= 0.0 or float_value > 1.0:
diff --git a/atomsci/ddm/pipeline/perf_data.py b/atomsci/ddm/pipeline/perf_data.py
index a8931c5a..42d1b2d7 100644
--- a/atomsci/ddm/pipeline/perf_data.py
+++ b/atomsci/ddm/pipeline/perf_data.py
@@ -7,10 +7,13 @@
 
 import deepchem as dc
 import numpy as np
+import logging
 from sklearn.metrics import roc_auc_score, confusion_matrix, average_precision_score, precision_score, recall_score
 from sklearn.metrics import accuracy_score, matthews_corrcoef, cohen_kappa_score, log_loss, balanced_accuracy_score
 from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error
 
+log = logging.getLogger('ATOM')
+
 # ******************************************************************************************************************************
 def rms_error(y_real, y_pred):
     """Calculates the root mean squared error. Score function used for model selection.
@@ -54,6 +57,234 @@ def negative_predictive_value(y_real, y_pred):
                           npv = negative_predictive_value, cross_entropy = log_loss, accuracy = accuracy_score, bal_accuracy = balanced_accuracy_score,
                           avg_precision = average_precision_score, mcc = matthews_corrcoef, kappa = cohen_kappa_score)
 
+MAX_INVALID_FRAC_TO_FILTER = 0.01
+_ACTIVE_MAX_INVALID_FRAC_TO_FILTER = MAX_INVALID_FRAC_TO_FILTER
+
+
+def _configure_invalid_pred_frac_threshold(params):
+    """Set module-level invalid prediction filtering threshold from params."""
+    global _ACTIVE_MAX_INVALID_FRAC_TO_FILTER
+    threshold = getattr(params, 'max_invalid_pred_frac', MAX_INVALID_FRAC_TO_FILTER)
+    if threshold is None:
+        threshold = MAX_INVALID_FRAC_TO_FILTER
+    threshold = float(threshold)
+    clamped_threshold = min(1.0, max(0.0, threshold))
+    if clamped_threshold != threshold:
+        log.warning(
+            "Clamped max_invalid_pred_frac from %.6f to %.6f; expected range is [0.0, 1.0].",
+            threshold,
+            clamped_threshold,
+        )
+    threshold = clamped_threshold
+    _ACTIVE_MAX_INVALID_FRAC_TO_FILTER = threshold
+    log.info("Using max_invalid_pred_frac=%.4f for invalid prediction handling.", threshold)
+
+
+def _get_active_invalid_pred_frac_threshold():
+    """Return active invalid prediction filtering threshold."""
+    return _ACTIVE_MAX_INVALID_FRAC_TO_FILTER
+
+
+def _regression_invalid_penalty(score_type):
+    """Return a hard penalty for invalid regression predictions.
+
+    Scores where larger is better (for example R2) get a very negative value.
+    Loss-like scores (for example MAE/RMSE) get a very large positive value.
+    """
+    if score_type == 'r2':
+        return -1.0e12
+    if score_type in {'mae', 'rmse'}:
+        return 1.0e12
+    return np.nan
+
+
+def _finite_row_mask(arr):
+    """Return a row-wise finite mask for 1D/2D+ arrays."""
+    arr = np.asarray(arr)
+    if arr.ndim <= 1:
+        return np.isfinite(arr)
+    reduce_axes = tuple(range(1, arr.ndim))
+    return np.all(np.isfinite(arr), axis=reduce_axes)
+
+
+def _invalid_stats(y_real, y_pred):
+    """Return invalid count, total count, and invalid fraction for paired arrays."""
+    y_real = np.asarray(y_real)
+    y_pred = np.asarray(y_pred)
+
+    if y_real.ndim == 0 or y_pred.ndim == 0 or y_real.shape[0] != y_pred.shape[0]:
+        return 0, 0, 0.0, np.array([], dtype=bool)
+
+    valid_rows = _finite_row_mask(y_real) & _finite_row_mask(y_pred)
+    total = int(valid_rows.shape[0])
+    invalid_count = int(total - np.sum(valid_rows))
+    invalid_frac = float(invalid_count / total) if total > 0 else 0.0
+    return invalid_count, total, invalid_frac, valid_rows
+
+
+def _safe_regression_score(score_type, y_real, y_pred, default=np.nan):
+    """Compute a regression metric with tolerant filtering then hard penalties.
+
+    If invalid values are at most 1% of rows, compute the metric on remaining rows.
+    Otherwise, return a score-specific worst-case penalty.
+    """
+    y_real = np.asarray(y_real).reshape(-1)
+    y_pred = np.asarray(y_pred).reshape(-1)
+    penalty = _regression_invalid_penalty(score_type)
+
+    threshold = _get_active_invalid_pred_frac_threshold()
+    invalid_count, total_count, invalid_frac, valid_rows = _invalid_stats(y_real, y_pred)
+    if invalid_frac > 0.0:
+        if invalid_frac <= threshold and np.any(valid_rows):
+            log.info(
+                "Filtered invalid regression rows for %s: %d/%d (%.2f%%) <= threshold %.2f%%",
+                score_type,
+                invalid_count,
+                total_count,
+                100.0 * invalid_frac,
+                100.0 * threshold,
+            )
+            y_real = y_real[valid_rows]
+            y_pred = y_pred[valid_rows]
+        else:
+            log.warning(
+                "Applying regression penalty for %s due to invalid rows: %d/%d (%.2f%%) > threshold %.2f%%",
+                score_type,
+                invalid_count,
+                total_count,
+                100.0 * invalid_frac,
+                100.0 * threshold,
+            )
+            return penalty
+
+    if score_type == 'r2' and y_real.size < 2:
+        return penalty
+
+    try:
+        return regr_score_func[score_type](y_real, y_pred)
+    except ValueError:
+        return penalty if np.isfinite(penalty) else default
+
+
+def _has_non_finite_values(arr):
+    """Return True when an array-like contains NaN/Inf numeric values."""
+    arr = np.asarray(arr)
+    if np.issubdtype(arr.dtype, np.number):
+        return not np.all(np.isfinite(arr))
+    if arr.dtype == object:
+        try:
+            return not np.all(np.isfinite(arr.astype(float)))
+        except (TypeError, ValueError):
+            return False
+    return False
+
+
+def _classification_invalid_penalty(score_type):
+    """Return a hard penalty for invalid classification predictions."""
+    if score_type == 'cross_entropy':
+        return 1.0e12
+    if score_type in {'mcc', 'kappa'}:
+        return -1.0
+    if score_type in classif_score_func:
+        return 0.0
+    return np.nan
+
+
+def _safe_classification_score(score_type, y_real, y_pred, average=None, default=np.nan):
+    """Compute a classification metric with tolerant filtering then hard penalties.
+
+    If invalid values are at most 1% of rows, compute the metric on remaining rows.
+    Otherwise, return a score-specific worst-case penalty.
+    """
+    penalty = _classification_invalid_penalty(score_type)
+    y_real = np.asarray(y_real)
+    y_pred = np.asarray(y_pred)
+
+    threshold = _get_active_invalid_pred_frac_threshold()
+
+    if _has_non_finite_values(y_real) or _has_non_finite_values(y_pred):
+        if y_real.ndim == 0 or y_pred.ndim == 0:
+            return penalty
+
+        if y_real.shape[0] != y_pred.shape[0]:
+            return penalty
+
+        invalid_count, total_count, invalid_frac, valid_rows = _invalid_stats(y_real, y_pred)
+        if invalid_frac <= threshold and np.any(valid_rows):
+            log.info(
+                "Filtered invalid classification rows for %s: %d/%d (%.2f%%) <= threshold %.2f%%",
+                score_type,
+                invalid_count,
+                total_count,
+                100.0 * invalid_frac,
+                100.0 * threshold,
+            )
+            y_real = y_real[valid_rows]
+            y_pred = y_pred[valid_rows]
+        else:
+            log.warning(
+                "Applying classification penalty for %s due to invalid rows: %d/%d (%.2f%%) > threshold %.2f%%",
+                score_type,
+                invalid_count,
+                total_count,
+                100.0 * invalid_frac,
+                100.0 * threshold,
+            )
+            return penalty
+
+    try:
+        if average is None:
+            return classif_score_func[score_type](y_real, y_pred)
+        return classif_score_func[score_type](y_real, y_pred, **{'average': average})
+    except ValueError:
+        return penalty if np.isfinite(penalty) else default
+
+
+def _coerce_invalid_pred_classes(y_real_classes, y_pred_classes, y_class_probs, num_classes):
+    """Force non-finite-probability rows to an explicitly wrong predicted class."""
+    pred_classes = np.asarray(y_pred_classes).copy()
+    real_classes = np.asarray(y_real_classes)
+    class_probs = np.asarray(y_class_probs)
+
+    if class_probs.ndim == 1:
+        invalid_rows = ~np.isfinite(class_probs)
+    else:
+        invalid_rows = ~np.all(np.isfinite(class_probs), axis=1)
+
+    if not np.any(invalid_rows):
+        return pred_classes
+
+    bad_idxs = np.where(invalid_rows)[0]
+    for idx in bad_idxs:
+        real_class = real_classes[idx]
+        if not np.isfinite(real_class):
+            pred_classes[idx] = 0
+            continue
+        real_class = int(real_class)
+        if num_classes == 2:
+            pred_classes[idx] = 1 - real_class if real_class in (0, 1) else 0
+        else:
+            pred_classes[idx] = (real_class + 1) % int(num_classes)
+
+    return pred_classes
+
+
+def _safe_confusion_matrix(y_real, y_pred, num_classes):
+    """Compute confusion matrix and return zeros when predictions are invalid."""
+    if _has_non_finite_values(y_real):
+        return np.zeros((num_classes, num_classes), dtype=int).tolist()
+
+    try:
+        return confusion_matrix(y_real, y_pred).tolist()
+    except ValueError:
+        return np.zeros((num_classes, num_classes), dtype=int).tolist()
+
+
+def _safe_mean(scores, default=np.nan):
+    """Average only finite values and return a fallback when none are valid."""
+    finite_scores = [score for score in scores if np.isfinite(score)]
+    return float(np.mean(finite_scores)) if finite_scores else default
+
 # The following score types are loss functions, meaning the result must be sign flipped so we can maximize it in model selection
 loss_funcs = {'mae', 'rmse', 'cross_entropy'}
 
@@ -91,6 +322,8 @@ def create_perf_data(prediction_type, model_dataset, subset, **kwargs):
         ValueError: if split_strategy not in ['train_valid_test','k_fold_cv']
         ValueError: prediction_type not in ['regression','classification']
     """
+    _configure_invalid_pred_frac_threshold(model_dataset.params)
+
     if subset == 'full':
         split_strategy = 'train_valid_test'
     else:
@@ -236,9 +469,9 @@ def model_choice_score(self, score_type='r2'):
             nzrows = np.where(weights[:,i] != 0)[0]
             task_real_vals = np.squeeze(real_vals[nzrows,i])
             task_pred_vals = np.squeeze(pred_vals[nzrows,i])
-            scores.append(regr_score_func[score_type](task_real_vals, task_pred_vals))
+            scores.append(_safe_regression_score(score_type, task_real_vals, task_pred_vals))
 
-        self.model_score = float(np.mean(scores))
+        self.model_score = _safe_mean(scores, default=-np.inf)
         if score_type in loss_funcs:
             self.model_score = -self.model_score
         return self.model_score
@@ -287,14 +520,14 @@ def get_prediction_results(self):
             nzrows = np.where(weights[:,i] != 0)[0]
             task_real_vals = np.squeeze(real_vals[nzrows,i])
             task_pred_vals = np.squeeze(pred_vals[nzrows,i])
-            mae_scores.append(regr_score_func['mae'](task_real_vals, task_pred_vals))
-            rms_scores.append(regr_score_func['rmse'](task_real_vals, task_pred_vals))
+            mae_scores.append(_safe_regression_score('mae', task_real_vals, task_pred_vals))
+            rms_scores.append(_safe_regression_score('rmse', task_real_vals, task_pred_vals))
             response_means.append(task_real_vals.mean().tolist())
             response_stds.append(task_real_vals.std().tolist())
-        pred_results['mae_score'] = float(np.mean(mae_scores))
+        pred_results['mae_score'] = _safe_mean(mae_scores)
         if self.num_tasks > 1:
             pred_results['task_mae_scores'] = mae_scores
-        pred_results['rms_score'] = float(np.mean(rms_scores))
+        pred_results['rms_score'] = _safe_mean(rms_scores)
         if self.num_tasks > 1:
             pred_results['task_rms_scores'] = rms_scores
 
@@ -306,6 +539,24 @@ def get_prediction_results(self):
         pred_results['mean_response_vals'] = response_means
         pred_results['std_response_vals'] = response_stds
 
+        task_invalid_counts = []
+        task_invalid_fracs = []
+        for i in range(self.num_tasks):
+            nzrows = np.where(weights[:,i] != 0)[0]
+            task_real_vals = np.squeeze(real_vals[nzrows,i])
+            task_pred_vals = np.squeeze(pred_vals[nzrows,i])
+            invalid_count, total_count, invalid_frac, _ = _invalid_stats(task_real_vals, task_pred_vals)
+            task_invalid_counts.append(int(invalid_count))
+            task_invalid_fracs.append(float(invalid_frac))
+
+        total_scored = int(np.sum(weights != 0))
+        total_invalid = int(np.sum(task_invalid_counts))
+        pred_results['invalid_prediction_count'] = total_invalid
+        pred_results['invalid_prediction_fraction'] = float(total_invalid / total_scored) if total_scored > 0 else 0.0
+        pred_results['invalid_prediction_threshold'] = _get_active_invalid_pred_frac_threshold()
+        pred_results['task_invalid_prediction_counts'] = task_invalid_counts
+        pred_results['task_invalid_prediction_fractions'] = task_invalid_fracs
+
         return pred_results
 
     # ****************************************************************************************
@@ -408,15 +659,15 @@ def model_choice_score(self, score_type='r2'):
         bind_real_vals = np.squeeze(real_vals[rowbind,0])
         bind_pred_vals = np.squeeze(pred_vals[rowbind,0])
         if len(rowki) > 0:
-            scores.append(regr_score_func[score_type](ki_real_vals, ki_pred_vals))
+            scores.append(_safe_regression_score(score_type, ki_real_vals, ki_pred_vals))
             if len(rowbind) > 0:
-                scores.append(regr_score_func[score_type](bind_real_vals, bind_pred_vals))
+                scores.append(_safe_regression_score(score_type, bind_real_vals, bind_pred_vals))
             else:
                 # if all values are dose response activities, use the r2_score above.
                 scores.append(scores[0])
         elif len(rowbind) > 0:
             # all values are single concentration activities.
-            scores.append(regr_score_func[score_type](bind_real_vals, bind_pred_vals))
+            scores.append(_safe_regression_score(score_type, bind_real_vals, bind_pred_vals))
             scores.append(scores[0])
 
         self.model_score = float(np.mean(scores))
@@ -476,19 +727,19 @@ def get_prediction_results(self):
         bind_real_vals = np.squeeze(real_vals[rowbind,0])
         bind_pred_vals = np.squeeze(pred_vals[rowbind,0])
         if len(rowki) > 0:
-            mae_scores.append(regr_score_func['mae'](ki_real_vals, ki_pred_vals))
-            rms_scores.append(regr_score_func['rmse'](ki_real_vals, ki_pred_vals))
+            mae_scores.append(_safe_regression_score('mae', ki_real_vals, ki_pred_vals))
+            rms_scores.append(_safe_regression_score('rmse', ki_real_vals, ki_pred_vals))
             if len(rowbind) > 0:
-                mae_scores.append(regr_score_func['mae'](bind_real_vals, bind_pred_vals))
-                rms_scores.append(regr_score_func['rmse'](bind_real_vals, bind_pred_vals))
+                mae_scores.append(_safe_regression_score('mae', bind_real_vals, bind_pred_vals))
+                rms_scores.append(_safe_regression_score('rmse', bind_real_vals, bind_pred_vals))
             else:
                 # if all values are dose response activities, use the r2_score above.
                 mae_scores.append(mae_scores[0])
                 rms_scores.append(rms_scores[0])
         elif len(rowbind) > 0:
             # all values are single concentration activities.
-            mae_scores.append(regr_score_func['mae'](bind_real_vals, bind_pred_vals))
-            rms_scores.append(regr_score_func['rmse'](bind_real_vals, bind_pred_vals))
+            mae_scores.append(_safe_regression_score('mae', bind_real_vals, bind_pred_vals))
+            rms_scores.append(_safe_regression_score('rmse', bind_real_vals, bind_pred_vals))
             mae_scores.append(mae_scores[0])
             rms_scores.append(rms_scores[0])
 
@@ -497,10 +748,10 @@ def get_prediction_results(self):
         response_means.append(bind_real_vals.mean().tolist())
         response_stds.append(bind_real_vals.std().tolist())
 
-        pred_results['mae_score'] = float(np.mean(mae_scores))
+        pred_results['mae_score'] = _safe_mean(mae_scores)
         if self.num_tasks > 1:
             pred_results['task_mae_scores'] = mae_scores
-        pred_results['rms_score'] = float(np.mean(rms_scores))
+        pred_results['rms_score'] = _safe_mean(rms_scores)
         if self.num_tasks > 1:
             pred_results['task_rms_scores'] = rms_scores
 
@@ -512,6 +763,20 @@ def get_prediction_results(self):
         pred_results['mean_response_vals'] = response_means
         pred_results['std_response_vals'] = response_stds
 
+        task_invalid_counts = []
+        task_invalid_fracs = []
+        ki_invalid_count, ki_total_count, ki_invalid_frac, _ = _invalid_stats(ki_real_vals, ki_pred_vals)
+        bind_invalid_count, bind_total_count, bind_invalid_frac, _ = _invalid_stats(bind_real_vals, bind_pred_vals)
+        task_invalid_counts.extend([int(ki_invalid_count), int(bind_invalid_count)])
+        task_invalid_fracs.extend([float(ki_invalid_frac), float(bind_invalid_frac)])
+        total_scored = int(ki_total_count + bind_total_count)
+        total_invalid = int(ki_invalid_count + bind_invalid_count)
+        pred_results['invalid_prediction_count'] = total_invalid
+        pred_results['invalid_prediction_fraction'] = float(total_invalid / total_scored) if total_scored > 0 else 0.0
+        pred_results['invalid_prediction_threshold'] = _get_active_invalid_pred_frac_threshold()
+        pred_results['task_invalid_prediction_counts'] = task_invalid_counts
+        pred_results['task_invalid_prediction_fractions'] = task_invalid_fracs
+
         return pred_results
 
     # ****************************************************************************************
@@ -628,12 +893,12 @@ def model_choice_score(self, score_type='roc_auc'):
                 task_pred_vars = task_class_probs
                 task_real_vars = task_real_vals
             else:
-                task_pred_vars = pred_classes[nzrows,i]
+                task_pred_classes = pred_classes[nzrows,i]
+                task_pred_vars = _coerce_invalid_pred_classes(
+                    task_real_classes, task_pred_classes, task_class_probs, self.num_classes
+                )
                 task_real_vars = task_real_classes
-            if average_param is None:
-                scores.append(classif_score_func[score_type](task_real_vars, task_pred_vars))
-            else:
-                scores.append(classif_score_func[score_type](task_real_vars, task_pred_vars, average=average_param))
+            scores.append(_safe_classification_score(score_type, task_real_vars, task_pred_vars, average=average_param))
 
         self.model_score = float(np.mean(scores))
         if score_type in loss_funcs:
@@ -683,42 +948,56 @@ def get_prediction_results(self):
         cross_entropies = []
         precisions = []
         recalls = []
-        if self.num_classes == 2:
-            npvs = []
+        npvs = []
         accuracies = []
         bal_accs = []
         kappas = []
         matthews_ccs = []
         confusion_matrices = []
+        task_invalid_counts = []
+        task_invalid_fracs = []
+        total_scored = 0
+        total_invalid = 0
         for i in range(self.num_tasks):
             nzrows = np.where(weights[:,i] != 0)[0]
-            task_pred_classes = pred_classes[nzrows,i]
             task_real_classes = real_classes[nzrows,i]
 
             if self.num_classes > 2:
                 # If more than 2 classes, task_real_vals is indicator matrix (one-hot encoded).
                 task_real_vals = real_vals[nzrows,i,:]
                 task_class_probs = class_probs[nzrows,i,:]
-                prc_aucs.append(average_precision_score(task_real_vals, task_class_probs, average='macro'))
-                precisions.append(float(precision_score(task_real_classes, task_pred_classes, average='macro')))
-                recalls.append(float(recall_score(task_real_classes, task_pred_classes, average='macro')))
+                invalid_count, total_count, invalid_frac, _ = _invalid_stats(task_real_vals, task_class_probs)
+                task_pred_classes = _coerce_invalid_pred_classes(
+                    task_real_classes, pred_classes[nzrows,i], task_class_probs, self.num_classes
+                )
+                prc_aucs.append(_safe_classification_score('avg_precision', task_real_vals, task_class_probs, average='macro'))
+                precisions.append(float(_safe_classification_score('precision', task_real_classes, task_pred_classes, average='macro')))
+                recalls.append(float(_safe_classification_score('recall', task_real_classes, task_pred_classes, average='macro')))
                 # NPV is not supported for multilabel classifiers, skip it
             else:
                 # sklearn metrics functions are expecting single array of 1s and 0s for task_real_vals
                 # and task_class_probs for class 1 only
                 task_real_vals = real_vals[nzrows,i]
                 task_class_probs = class_probs[nzrows,i,1]
-                prc_aucs.append(average_precision_score(task_real_vals, task_class_probs))
-                precisions.append(float(precision_score(task_real_vals, task_pred_classes, average='binary')))
-                recalls.append(float(recall_score(task_real_vals, task_pred_classes, average='binary')))
-                npvs.append(negative_predictive_value(task_real_vals, task_pred_classes))
-
-            cross_entropies.append(log_loss(task_real_vals, task_class_probs))
-            accuracies.append(accuracy_score(task_real_classes, task_pred_classes))
-            bal_accs.append(balanced_accuracy_score(task_real_classes, task_pred_classes))
-            kappas.append(float(cohen_kappa_score(task_real_classes, task_pred_classes)))
-            matthews_ccs.append(float(matthews_corrcoef(task_real_classes, task_pred_classes)))
-            confusion_matrices.append(confusion_matrix(task_real_classes, task_pred_classes).tolist())
+                invalid_count, total_count, invalid_frac, _ = _invalid_stats(task_real_vals, task_class_probs)
+                task_pred_classes = _coerce_invalid_pred_classes(
+                    task_real_classes, pred_classes[nzrows,i], task_class_probs, self.num_classes
+                )
+                prc_aucs.append(_safe_classification_score('avg_precision', task_real_vals, task_class_probs))
+                precisions.append(float(_safe_classification_score('precision', task_real_vals, task_pred_classes, average='binary')))
+                recalls.append(float(_safe_classification_score('recall', task_real_vals, task_pred_classes, average='binary')))
+                npvs.append(float(_safe_classification_score('npv', task_real_vals, task_pred_classes)))
+
+            cross_entropies.append(_safe_classification_score('cross_entropy', task_real_vals, task_class_probs))
+            accuracies.append(_safe_classification_score('accuracy', task_real_classes, task_pred_classes))
+            bal_accs.append(_safe_classification_score('bal_accuracy', task_real_classes, task_pred_classes))
+            kappas.append(float(_safe_classification_score('kappa', task_real_classes, task_pred_classes)))
+            matthews_ccs.append(float(_safe_classification_score('mcc', task_real_classes, task_pred_classes)))
+            confusion_matrices.append(_safe_confusion_matrix(task_real_classes, task_pred_classes, self.num_classes))
+            task_invalid_counts.append(int(invalid_count))
+            task_invalid_fracs.append(float(invalid_frac))
+            total_scored += int(total_count)
+            total_invalid += int(invalid_count)
 
         pred_results['prc_auc_score'] = float(np.mean(prc_aucs))
         if self.num_tasks > 1:
@@ -763,6 +1042,11 @@ def get_prediction_results(self):
 
         pred_results['confusion_matrix'] = confusion_matrices
         pred_results['num_compounds'] = self.num_cmpds
+        pred_results['invalid_prediction_count'] = int(total_invalid)
+        pred_results['invalid_prediction_fraction'] = float(total_invalid / total_scored) if total_scored > 0 else 0.0
+        pred_results['invalid_prediction_threshold'] = _get_active_invalid_pred_frac_threshold()
+        pred_results['task_invalid_prediction_counts'] = task_invalid_counts
+        pred_results['task_invalid_prediction_fractions'] = task_invalid_fracs
 
         return pred_results
 
@@ -1155,13 +1439,13 @@ def accumulate_preds(self, predicted_vals, ids, pred_stds=None):
                 # If more than 2 classes, real_vals is indicator matrix (one-hot encoded). 
                 task_real_vals = np.squeeze(real_vals[nzrows,i,:])
                 task_class_probs =np.squeeze(class_probs[nzrows,i,:])
-                scores.append(roc_auc_score(task_real_vals, task_class_probs, average='macro'))
+                scores.append(_safe_classification_score('roc_auc', task_real_vals, task_class_probs, average='macro'))
             else:
                 # For binary classifier, sklearn metrics functions are expecting single array of 1s and 0s for real_vals_list,
                 # and class_probs for class 1 only.
                 task_real_vals = np.squeeze(real_vals[nzrows,i])
                 task_class_probs = np.squeeze(class_probs[nzrows,i,1])
-                scores.append(roc_auc_score(task_real_vals, task_class_probs))
+                scores.append(_safe_classification_score('roc_auc', task_real_vals, task_class_probs))
         self.perf_metrics.append(np.array(scores))
         return float(np.mean(scores))
 
@@ -1370,9 +1654,9 @@ def accumulate_preds(self, predicted_vals, ids=None, pred_stds=None):
             nzrows = np.where(weights[:,i] != 0)[0]
             task_real_vals = np.squeeze(real_vals[nzrows,i])
             task_pred_vals = np.squeeze(pred_vals[nzrows,i])
-            scores.append(r2_score(task_real_vals, task_pred_vals))
+            scores.append(_safe_regression_score('r2', task_real_vals, task_pred_vals))
         self.perf_metrics.append(np.array(scores))
-        return float(np.mean(scores))
+        return _safe_mean(scores)
 
 
     # ****************************************************************************************
@@ -1591,13 +1875,13 @@ def accumulate_preds(self, predicted_vals, ids, pred_stds=None):
                 task_real_vals = np.squeeze(real_vals[nzrows,i,:])
                 task_class_probs = np.squeeze(class_probs[nzrows,i,:])
 
-                scores.append(roc_auc_score(task_real_vals, task_class_probs, average='macro'))
+                scores.append(_safe_classification_score('roc_auc', task_real_vals, task_class_probs, average='macro'))
             else:
                 # For binary classifier, sklearn metrics functions are expecting single array of 1s and 0s for real_vals_list,
                 # and class_probs for class 1 only.
                 task_real_vals = np.squeeze(real_vals[nzrows,i])
                 task_class_probs = np.squeeze(class_probs[nzrows,i,1])
-                scores.append(roc_auc_score(task_real_vals, task_class_probs))
+                scores.append(_safe_classification_score('roc_auc', task_real_vals, task_class_probs))
         self.perf_metrics.append(np.array(scores))
         return float(np.mean(scores))
 
@@ -1800,19 +2084,19 @@ def accumulate_preds(self, predicted_vals, ids, pred_stds=None):
         bind_real_vals = np.squeeze(real_vals[rowbind,0])
         bind_pred_vals = np.squeeze(pred_vals[rowbind,0])
         if len(rowki) > 0:
-            scores.append(r2_score(ki_real_vals, ki_pred_vals))
+            scores.append(_safe_regression_score('r2', ki_real_vals, ki_pred_vals))
             if len(rowbind) > 0:
-                scores.append(r2_score(bind_real_vals, bind_pred_vals))
+                scores.append(_safe_regression_score('r2', bind_real_vals, bind_pred_vals))
             else:
                 # if all values are dose response activities, use the r2_score above.
                 scores.append(scores[0])
         elif len(rowbind) > 0:
             # all values are single concentration activities.
-            scores.append(r2_score(bind_real_vals, bind_pred_vals))
+            scores.append(_safe_regression_score('r2', bind_real_vals, bind_pred_vals))
             scores.append(scores[0])
 
         self.perf_metrics.append(np.array(scores))
-        return float(np.mean(scores))
+        return _safe_mean(scores)
 
     # ****************************************************************************************
     # class SimpleHybridPerfData
diff --git a/atomsci/ddm/pipeline/perf_plots.py b/atomsci/ddm/pipeline/perf_plots.py
index bd0b03f9..e6f0b9d4 100644
--- a/atomsci/ddm/pipeline/perf_plots.py
+++ b/atomsci/ddm/pipeline/perf_plots.py
@@ -12,6 +12,7 @@
 import seaborn as sns
 import umap
 import sklearn.metrics as metrics
+import warnings
 from sklearn.metrics import ConfusionMatrixDisplay, PrecisionRecallDisplay
 import matplotlib.pyplot as plt
 from matplotlib.backends.backend_pdf import PdfPages
@@ -86,7 +87,8 @@ def plot_pred_vs_actual(model, epoch_label='best', threshold=None, error_bars=Fa
         None
     """
     if isinstance(model, str):
-        return plot_pred_vs_actual_from_file(model, plot_size=plot_size, external_training_data=external_training_data, error_bars=error_bars)
+        plot_pred_vs_actual_from_file(model, plot_size=plot_size, external_training_data=external_training_data, error_bars=error_bars)
+        return
     elif not isinstance(model, mp.ModelPipeline):
         raise ValueError('model must be either a ModelPipeline or a path to a saved model')
     params = model.params
@@ -266,8 +268,7 @@ def plot_pred_vs_actual_from_df(pred_df, actual_col='avg_pIC50_actual', pred_col
 
 #------------------------------------------------------------------------------------------------------------------------
 def plot_pred_vs_actual_from_file(model_path, external_training_data=None, plot_size=7, error_bars=False, threshold=None):
-    """Plot predicted vs actual values from a trained regression model from a model tarball. 
-    This function only works for locally trained models; otherwise see the `predict_from_model` module.
+    """Plot predicted vs actual values from a trained regression model from a model tarball. This function only works for locally trained models; otherwise see the predict_from_model module.
 
     Args:
         model_path (str): Path to an AMPL model tar.gz file.
@@ -276,7 +277,7 @@ def plot_pred_vs_actual_from_file(model_path, external_training_data=None, plot_
         error_bars (bool): whether to plot uncertainty as shaded region above and below the predicted values.
 
     Returns:
-        None
+        Matplotlib Figure
 
     Effects:
         A matplotlib figure is displayed with subplots for each response column and train/valid/test subsets.
@@ -286,100 +287,163 @@ def plot_pred_vs_actual_from_file(model_path, external_training_data=None, plot_
     reload_dir = tempfile.mkdtemp()
     with tarfile.open(model_path, mode='r:gz') as tar:
         futils.safe_extract(tar, path=reload_dir)
-    
+
     # reload metadata
     with open(os.path.join(reload_dir, 'model_metadata.json')) as f:
-        config=json.loads(f.read())
+        config = json.loads(f.read())
 
-    if config['model_parameters']['prediction_type']=='classification':
-        raise ValueError("plot_pred_vs_actual_from_file() should only be called for regression models. Please try plot_confusion_matrices() instead.")
-    
-    # load (featurized) data
-    dataset_dict=config['training_dataset']
+    if config['model_parameters']['prediction_type'] == 'classification':
+        raise ValueError(
+            "plot_pred_vs_actual_from_file() should only be called for regression models. "
+            "Please try plot_confusion_matrices() instead."
+        )
+
+    # dataset info
+    dataset_dict = config['training_dataset']
     if external_training_data is not None:
-        dataset_dict['dataset_key']=external_training_data
-    dataset_key=dataset_dict['dataset_key']
-    dataset_name = os.path.splitext(os.path.basename(dataset_key))[0]
+        dataset_dict['dataset_key'] = external_training_data
 
-    is_featurized=False
-    AD_method=None
+    original_dataset_key = dataset_dict['dataset_key']
+    dataset_name = os.path.splitext(os.path.basename(original_dataset_key))[0]
+
+    response_cols = dataset_dict['response_cols']
+    id_col = dataset_dict['id_col']
+    smiles_col = dataset_dict['smiles_col']
+
+    # determine featurized path
+    is_featurized = False
+    AD_method = None
     model_type = config['model_parameters']['model_type']
     featurizer = config['model_parameters']['featurizer']
-    if featurizer in ['descriptors','computed_descriptors']:
-        desc=config['descriptor_specific']['descriptor_type']
-        dataset_key=dataset_key.rsplit('/', maxsplit=1)
-        dataset_key=os.path.join(dataset_key[0], 'scaled_descriptors', dataset_key[1].replace('.csv',f'_with_{desc}_descriptors.csv'))
-        is_featurized=True
+
+    dataset_key_to_read = original_dataset_key
+    if featurizer in ['descriptors', 'computed_descriptors']:
+        desc = config['descriptor_specific']['descriptor_type']
+        parts = original_dataset_key.rsplit('/', maxsplit=1)
+        dataset_key_to_read = os.path.join(
+            parts[0],
+            'scaled_descriptors',
+            parts[1].replace('.csv', f'_with_{desc}_descriptors.csv')
+        )
+        is_featurized = True
         features_label = f"{desc} descriptors"
     else:
         features_label = f"{featurizer} features"
-    df=pd.read_csv(dataset_key)
-    
-    # reload split file
-    dataset_key=dataset_dict['dataset_key']
-    split_dict=config['splitting_parameters']
+
+    # load dataframes
+    df = pd.read_csv(dataset_key_to_read)
+    orig_df = pd.read_csv(original_dataset_key)
+
+    # sanity-check alignment and merge missing response columns
+    df = merge_response_cols_from_original(
+        df,
+        orig_df,
+        id_col=id_col,
+        response_cols=response_cols,
+        max_missing_frac=0.01,
+        error_on_extra_feat_ids=False,
+        coerce_id_to_str=True,
+        sample_n=20,
+    )
+
+    # reload split file (use original dataset location)
+    split_dict = config['splitting_parameters']
     split_uuid = split_dict['split_uuid']
-    splitter=split_dict['splitter']
-    split_strategy=split_dict['split_strategy']
-    data_dir=os.path.dirname(os.path.realpath(dataset_key))
-    split=[file for file in os.listdir(data_dir) if split_uuid in file]
-    split_file=os.path.join(data_dir, split[0])
+    splitter = split_dict['splitter']
+    split_strategy = split_dict['split_strategy']
+
+    data_dir = os.path.dirname(os.path.realpath(original_dataset_key))
+    split = [file for file in os.listdir(data_dir) if split_uuid in file]
+    split_file = os.path.join(data_dir, split[0])
+
     split_subsets = ['train', 'valid', 'test']
-    split=pd.read_csv(split_file)
-    split=split.rename(columns={'cmpd_id':dataset_dict['id_col']})
-    
-    # merge
-    df=df.merge(split, how='left')
-    
-    # get other values
-    response_cols=dataset_dict['response_cols']
-    
+    split = pd.read_csv(split_file)
+    split = split.rename(columns={'cmpd_id': id_col})
+
+    # merge split labels
+    if id_col in df.columns:
+        df[id_col] = df[id_col].astype(str)
+    if id_col in split.columns:
+        split[id_col] = split[id_col].astype(str)
+
+    df = df.merge(split, how='left', on=id_col)
+
     # run predictions
-    pred_df=pfm.predict_from_model_file(model_path, df, id_col=dataset_dict['id_col'], smiles_col=dataset_dict['smiles_col'], 
-                                        response_col=response_cols, is_featurized=is_featurized, AD_method=AD_method, dont_standardize=True)          
-    
+    pred_df = pfm.predict_from_model_file(
+        model_path,
+        df,
+        id_col=id_col,
+        smiles_col=smiles_col,
+        response_col=response_cols,
+        is_featurized=is_featurized,
+        AD_method=AD_method,
+        dont_standardize=True
+    )
+
     # plot
     sns.set_context('notebook')
     nss = len(split_subsets)
-    fig, axes = plt.subplots(len(response_cols), nss, figsize=(plot_size*nss, plot_size*len(response_cols)))
-    if error_bars and config['model_parameters']['uncertainty']:
-        suptitle = f"{dataset_name} {splitter} {split_strategy} split {model_type} model on {features_label}, predicted vs actual values with uncertainty"
+    fig, axes = plt.subplots(len(response_cols), nss, figsize=(plot_size * nss, plot_size * len(response_cols)))
+
+    if error_bars and config['model_parameters'].get('uncertainty'):
+        suptitle = (
+            f"{dataset_name} {splitter} {split_strategy} split {model_type} model on {features_label}, "
+            f"predicted vs actual values with uncertainty"
+        )
     else:
-        error_bars=False
-        suptitle = f"{dataset_name} {splitter} {split_strategy} split {model_type} model on {features_label}, predicted vs actual values"
+        error_bars = False
+        suptitle = (
+            f"{dataset_name} {splitter} {split_strategy} split {model_type} model on {features_label}, "
+            f"predicted vs actual values"
+        )
+
     fig.suptitle(suptitle, y=0.95)
     axes = axes.flatten()
-    for i,resp in enumerate(response_cols):
+
+    for i, resp in enumerate(response_cols):
         actual_col = f'{resp}_actual'
         pred_col = f'{resp}_pred'
         task_pred_df = pred_df[pred_df[actual_col].notna() & pred_df[pred_col].notna()]
+
         y_actual = task_pred_df[actual_col].values
         y_pred = task_pred_df[pred_col].values
+
         if error_bars:
             std_col = f'{resp}_std'
             y_std = task_pred_df[std_col].values
         else:
-            std_col=None
+            std_col = None
             y_std = 0
-        ymin = min(min(y_actual), min(y_pred), min(y_pred-y_std))
-        ymax = max(max(y_actual), max(y_pred), max(y_pred+y_std))
+
+        ymin = min(min(y_actual), min(y_pred), min(y_pred - y_std))
+        ymax = max(max(y_actual), max(y_pred), max(y_pred + y_std))
+
         for j, subset in enumerate(split_subsets):
-            ax = axes[nss*i + j]
-            tmp = task_pred_df[task_pred_df.subset==subset]
+            ax = axes[nss * i + j]
+            tmp = task_pred_df[task_pred_df.subset == subset]
             r2 = metrics.r2_score(tmp[actual_col].values, tmp[pred_col].values)
+
             ax.set_xlabel(f"Actual {resp}")
             ax.set_ylabel(f"Predicted {resp}")
-            # Set subplot title
+
             if j == 0:
                 subtitle = f"{resp} {subset}, {score_type_label['r2']} = {r2:.3f}"
             else:
                 subtitle = f"{subset}, {score_type_label['r2']} = {r2:.3f}"
-            ax=plot_pred_vs_actual_from_df(tmp, actual_col=actual_col, pred_col=pred_col, std_col = std_col, label=subtitle, threshold=threshold, ax=ax)
-            # Force axes to have same scale for all subsets for same task (but not different tasks!)
-            # wont work unless applied after the graph is created
+
+            ax = plot_pred_vs_actual_from_df(
+                tmp,
+                actual_col=actual_col,
+                pred_col=pred_col,
+                std_col=std_col,
+                label=subtitle,
+                threshold=threshold,
+                ax=ax
+            )
             ax.set_xlim(ymin, ymax)
             ax.set_ylim(ymin, ymax)
 
+    return fig
 
 #------------------------------------------------------------------------------------------------------------------------
 def plot_perf_vs_epoch(MP, plot_size=7, pdf_dir=None):
@@ -1259,3 +1323,100 @@ def plot_umap_train_set_neighbors(MP, num_neighbors=20, min_dist=0.1,
     if pdf_dir is not None:
         pdf.close()
         MP.log.info("Wrote plot to %s" % pdf_path)
+
+#------------------------------------------------------------------------------------------------------------------------
+def merge_response_cols_from_original(
+    feat_df: pd.DataFrame,
+    orig_df: pd.DataFrame,
+    *,
+    id_col: str,
+    response_cols: list,
+    max_missing_frac: float = 0.01,
+    error_on_extra_feat_ids: bool = False,
+    coerce_id_to_str: bool = True,
+    sample_n: int = 20,
+) -> pd.DataFrame:
+    """
+    Sanity-check compound alignment between a featurized dataframe and the original dataframe,
+    then merge missing response columns from the original into the featurized dataframe.
+
+    Expected behavior:
+      - Some original compounds may be missing in feat_df due to featurization failures.
+      - This missing fraction must be <= max_missing_frac, otherwise raise.
+      - If feat_df contains IDs not found in orig_df, warn or raise (configurable).
+
+    Returns:
+        A copy of feat_df with missing response columns merged in (left join on id_col).
+    """
+    if id_col not in feat_df.columns:
+        raise KeyError(f"Featurized dataframe is missing id_col '{id_col}'.")
+    if id_col not in orig_df.columns:
+        raise KeyError(f"Original dataframe is missing id_col '{id_col}'.")
+
+    feat = feat_df.copy()
+    orig = orig_df.copy()
+
+    if coerce_id_to_str:
+        feat[id_col] = feat[id_col].astype(str)
+        orig[id_col] = orig[id_col].astype(str)
+
+    feat_ids = pd.Series(feat[id_col]).dropna()
+    orig_ids = pd.Series(orig[id_col]).dropna()
+
+    n_feat_unique = feat_ids.nunique()
+    n_orig_unique = orig_ids.nunique()
+
+    feat_set = set(feat_ids.unique())
+    orig_set = set(orig_ids.unique())
+
+    missing_in_feat = orig_set - feat_set
+    extra_in_feat = feat_set - orig_set
+
+    missing_frac = len(missing_in_feat) / len(orig_set)
+    if missing_frac > max_missing_frac:
+        sample = list(sorted(missing_in_feat))[:sample_n]
+        raise ValueError(
+            f"Featurized dataframe appears incomplete relative to original: "
+            f"{len(missing_in_feat)}/{len(orig_set)} unique compounds missing "
+            f"({missing_frac:.2%}), exceeds allowed {max_missing_frac:.2%}. "
+            f"Examples: {sample}"
+        )
+
+    if len(extra_in_feat) > 0:
+        sample = list(sorted(extra_in_feat))[:sample_n]
+        msg = (
+            f"Featurized dataframe contains {len(extra_in_feat)} unique compound IDs not found in original. "
+            f"This may indicate mismatched files or inconsistent '{id_col}' values. "
+            f"Examples: {sample}"
+        )
+        if error_on_extra_feat_ids:
+            raise ValueError(msg)
+        warnings.warn(msg)
+
+    # Duplicate warnings (merge safety)
+    dup_feat = len(feat) - n_feat_unique
+    dup_orig = len(orig) - n_orig_unique
+    if dup_feat > 0:
+        warnings.warn(
+            f"Featurized dataframe has {dup_feat} duplicate '{id_col}' rows "
+            f"({len(feat)} rows, {n_feat_unique} unique IDs)."
+        )
+    if dup_orig > 0:
+        warnings.warn(
+            f"Original dataframe has {dup_orig} duplicate '{id_col}' rows "
+            f"({len(orig)} rows, {n_orig_unique} unique IDs)."
+        )
+
+    # Merge in only missing response columns (do not overwrite existing)
+    available_responses = [c for c in response_cols if c in orig.columns]
+    if not available_responses:
+        return feat
+
+    missing_response_cols = [c for c in available_responses if c not in feat.columns]
+    if not missing_response_cols:
+        return feat
+
+    orig_subset = orig[[id_col] + missing_response_cols].drop_duplicates(subset=[id_col])
+    merged = feat.merge(orig_subset, how='left', on=id_col)
+
+    return merged
diff --git a/atomsci/ddm/pipeline/transformations.py b/atomsci/ddm/pipeline/transformations.py
index 7f42f003..a81e4403 100644
--- a/atomsci/ddm/pipeline/transformations.py
+++ b/atomsci/ddm/pipeline/transformations.py
@@ -5,11 +5,11 @@
 import logging
 
 import numpy as np
-import umap
+import pandas as pd
 
 from deepchem.trans.transformers import Transformer, NormalizationTransformer, BalancingTransformer
-from sklearn.impute import SimpleImputer
-from sklearn.preprocessing import RobustScaler
+
+import sklearn.utils as sku
 
 logging.basicConfig(format='%(asctime)-15s %(message)s')
 log = logging.getLogger('ATOM')
@@ -80,18 +80,12 @@ def create_feature_transformers(params, featurization, train_dset):
     Returns:
         (list of DeepChem transformer objects): list of transformers for the feature matrix
     """
-    if params.feature_transform_type == 'umap':
-        # Map feature vectors using UMAP for dimension reduction
-        log.warning("UMAP feature transformation is deprecated and will be removed in a future release.")
-        if params.split_strategy == 'k_fold_cv':
-            log.warning("Warning: UMAP transformation may produce misleading results when used with K-fold split strategy.")
-        transformers_x = [UMAPTransformer(params, train_dset)]
-    elif params.transformers:
+    if params.transformers:
         # TODO: Transformers on responses and features should be controlled only by parameters
         # response_transform_type and feature_transform_type, rather than params.transformers.
 
         # Scale and center feature matrix if featurization type calls for it
-        transformers_x = featurization.create_feature_transformer(train_dset)
+        transformers_x = featurization.create_feature_transformer(train_dset, params)
     else:
         transformers_x = []
 
@@ -132,16 +126,21 @@ def get_transformer_specific_metadata(params):
         meta_dict (dict): Nested dictionary of parameters and values for each currently active
         transformer.
     """
-    meta_dict = {}
-    if params.feature_transform_type == 'umap':
-        umap_dict = dict(
-                        umap_dim = params.umap_dim,
-                        umap_metric = params.umap_metric,
-                        umap_targ_wt = params.umap_targ_wt,
-                        umap_neighbors = params.umap_neighbors,
-                        umap_min_dist = params.umap_min_dist )
-        meta_dict['umap_specific'] = umap_dict
-    return meta_dict
+    meta_dict = dict(
+        robustscaler_with_centering = params.robustscaler_with_centering,
+        robustscaler_with_scaling = params.robustscaler_with_scaling,
+        robustscaler_quartile_range = params.robustscaler_quartile_range,
+        robustscaler_unit_variance = params.robustscaler_unit_variance,
+        powertransformer_method = params.powertransformer_method,
+        powertransformer_standardize = params.powertransformer_standardize,
+        imputer_strategy = params.imputer_strategy)
+
+    # this parameter is set only if transformations have been loaded
+    # from an outside source
+    if hasattr(params, 'transformer_dataset_key_configs'):
+        meta_dict['transformer_dataset_key_configs'] = params.transformer_dataset_key_configs
+
+    return dict(transformer_specific=meta_dict)
 
 # ****************************************************************************************
 def get_transformer_keys(params):
@@ -207,62 +206,79 @@ def get_all_training_datasets(model_dataset):
 
 
 # ****************************************************************************************
-class UMAPTransformer(Transformer):
-    """Dimension reduction transformations using the UMAP algorithm.
-    
-    DEPRECATED: Will be removed in a future release.
+def zero_out_inf_nan(d):
+    """Return a copy of d with NaN and +/-Inf replaced by 0.
+
+    Supports numpy arrays and pandas Series/DataFrame.
+    """
+    # Handle pandas structures
+    if isinstance(d, (pd.Series, pd.DataFrame)):
+        arr = d.values.copy()
+        mask = ~np.isfinite(arr)
+        if mask.any():
+            arr[mask] = 0.0
+        if isinstance(d, pd.Series):
+            return pd.Series(arr, index=d.index, name=d.name)
+        else:
+            return pd.DataFrame(arr, index=d.index, columns=d.columns)
+    else:
+        # Fallback to numpy array
+        arr = np.array(d, copy=True)
+        mask = ~np.isfinite(arr)
+        if mask.any():
+            arr[mask] = 0.0
+        return arr
 
-    Attributes:
-        mapper (UMAP) : UMAP transformer
-        scaler (RobustScaler): Centering/scaling transformer
 
+class SklearnPipelineWrapper(Transformer):
+    """
+    This wraps a given sklearn transformer and converts it to a DeepChem style transformer
     """
-    def __init__(self, params, dataset):
-        """Initializes a UMAPTransformer object.
+    def __init__(self, dataset, sklearn_pipeline, 
+                 transform_X=False, transform_y=False, transform_w=False):
 
-        Args:
-            params (Namespace): Contains parameters used to instantiate the transformer.
+        self.transform_X = transform_X
+        self.transform_y = transform_y
+        self.transform_w = transform_w
 
-            dataset (Dataset): Dataset used to "train" the projection mapping.
-        """
+        assert (self.transform_X ^ self.transform_y) ^ self.transform_w, \
+            "This transformer can operate on only one of X, y, or w."
 
-        # TODO: decide whether to make n_epochs a parameter
-        #default_n_epochs = None
-        default_n_epochs = 500
+        self.sklearn_pipeline = sklearn_pipeline
 
-        if params.prediction_type == 'classification':
-            target_metric = 'categorical'
+        if self.transform_X:
+            data = dataset.X
+        elif self.transform_y:
+            data = dataset.y
         else:
-            target_metric = 'l2'
-        self.scaler = RobustScaler()
-        # Use SimpleImputer to replace missing values (NaNs) with means for each column
-        self.imputer = SimpleImputer()
-        scaled_X = self.scaler.fit_transform(self.imputer.fit_transform(dataset.X))
-        self.mapper = umap.UMAP(n_neighbors=params.umap_neighbors,
-                                n_components=params.umap_dim,
-                                metric=params.umap_metric,
-                                target_metric=target_metric,
-                                target_weight=params.umap_targ_wt,
-                                min_dist=params.umap_min_dist,
-                                n_epochs=default_n_epochs)
-        # TODO: How to deal with multitask data?
-        self.mapper.fit(scaled_X, y=dataset.y.flatten())
-
-    # ****************************************************************************************
+            data = dataset.w
+
+        # Fit the sklearn pipeline
+        try:
+            sku.assert_all_finite(data)
+        except ValueError:
+            # data contains inf or nan values
+            log.warning("SklearnPipelineWrapper: data contains NaN or Inf; replacing with zeros")
+            data = zero_out_inf_nan(data)
+
+        self.sklearn_pipeline.fit(data)
+
     def transform(self, dataset, parallel=False):
-        return super(UMAPTransformer, self).transform(dataset, parallel=parallel)
+        return dataset.transform(self)
 
-    # ****************************************************************************************
     def transform_array(self, X, y, w, ids):
-        X = self.mapper.transform(self.scaler.transform(self.imputer.transform(X)))
-        return (X, y, w, ids)
+        """Transform the data in a set of (X, y, w) arrays."""
+        if self.transform_X:
+            X = self.sklearn_pipeline.transform(X)
+        elif self.transform_y:
+            y = self.sklearn_pipeline.transform(y)
+        else:
+            w = self.sklearn_pipeline.transform(w)
 
-    # ****************************************************************************************
-    def untransform(self, z):
-        """Reverses stored transformation on provided data."""
-        raise NotImplementedError("Can't reverse a UMAP transformation")
-    # ****************************************************************************************
+        return (X, y, w, ids)
 
+    def untransform(self, z: np.ndarray) -> np.ndarray:
+        raise NotImplementedError("SklearnPipelineWrapper does not support inverse transforms")
 
 # ****************************************************************************************
 
@@ -310,7 +326,7 @@ def transform_array(self, X, y, w, ids):
                 X = np.nan_to_num(X * X_weight / self.X_stds)
             # zero out large values, especially for out of range test data
             X[np.abs(X) > 1e30] = 1e30
-        if self.transform_y:
+        elif self.transform_y:
             if not hasattr(self, 'move_mean') or self.move_mean:
                 y = np.nan_to_num((y - self.y_means) / self.y_stds)
             else:
diff --git a/atomsci/ddm/test/integrative/balancing_trans/jsons/Identity_transformer.json b/atomsci/ddm/test/integrative/balancing_trans/jsons/Identity_transformer.json
new file mode 100644
index 00000000..45870aef
--- /dev/null
+++ b/atomsci/ddm/test/integrative/balancing_trans/jsons/Identity_transformer.json
@@ -0,0 +1,24 @@
+{
+        "dataset_key" : "replaced",
+        "datastore" : "False",
+        "uncertainty": "False",
+        "splitter": "scaffold",
+        "split_valid_frac": "0.20",
+        "split_test_frac": "0.20",
+        "split_strategy": "train_valid_test",
+        "prediction_type": "classification",
+        "model_choice_score_type": "roc_auc",
+        "response_cols" : "active",
+        "id_col": "compound_id",
+        "smiles_col" : "rdkit_smiles",
+        "result_dir": "replaced",
+        "system": "LC",
+        "transformers": "True",
+        "model_type": "RF",
+        "featurizer": "computed_descriptors",
+        "descriptor_type": "mordred_filtered",
+        "feature_transform_type": "Identity",
+        "save_results": "False",
+        "verbose": "False",
+        "seed":"0"
+    }
\ No newline at end of file
diff --git a/atomsci/ddm/test/integrative/balancing_trans/jsons/PowerTransformer_transformer.json b/atomsci/ddm/test/integrative/balancing_trans/jsons/PowerTransformer_transformer.json
new file mode 100644
index 00000000..851deb1d
--- /dev/null
+++ b/atomsci/ddm/test/integrative/balancing_trans/jsons/PowerTransformer_transformer.json
@@ -0,0 +1,24 @@
+{
+        "dataset_key" : "replaced",
+        "datastore" : "False",
+        "uncertainty": "False",
+        "splitter": "scaffold",
+        "split_valid_frac": "0.20",
+        "split_test_frac": "0.20",
+        "split_strategy": "train_valid_test",
+        "prediction_type": "classification",
+        "model_choice_score_type": "roc_auc",
+        "response_cols" : "active",
+        "id_col": "compound_id",
+        "smiles_col" : "rdkit_smiles",
+        "result_dir": "replaced",
+        "system": "LC",
+        "transformers": "True",
+        "model_type": "RF",
+        "featurizer": "computed_descriptors",
+        "descriptor_type": "mordred_filtered",
+        "feature_transform_type": "PowerTransformer",
+        "save_results": "False",
+        "verbose": "False",
+        "seed":"0"
+    }
\ No newline at end of file
diff --git a/atomsci/ddm/test/integrative/balancing_trans/jsons/RobustScaler_transformer.json b/atomsci/ddm/test/integrative/balancing_trans/jsons/RobustScaler_transformer.json
new file mode 100644
index 00000000..9a171a04
--- /dev/null
+++ b/atomsci/ddm/test/integrative/balancing_trans/jsons/RobustScaler_transformer.json
@@ -0,0 +1,29 @@
+{
+        "dataset_key" : "replaced",
+        "datastore" : "False",
+        "uncertainty": "False",
+        "splitter": "scaffold",
+        "split_valid_frac": "0.20",
+        "split_test_frac": "0.20",
+        "split_strategy": "train_valid_test",
+        "prediction_type": "classification",
+        "model_choice_score_type": "roc_auc",
+        "response_cols" : "active",
+        "id_col": "compound_id",
+        "smiles_col" : "rdkit_smiles",
+        "result_dir": "replaced",
+        "system": "LC",
+        "transformers": "True",
+        "model_type": "RF",
+        "featurizer": "computed_descriptors",
+        "descriptor_type": "mordred_filtered",
+        "feature_transform_type": "RobustScaler",
+        "robustscaler_with_center": "True",
+        "robustscaler_with_scaling": "True",
+        "robustscaler_quartile_range": "30.0,80.0",
+        "robustscaler_unit_variance": "True",
+        "imputer_strategy": "median",
+        "save_results": "False",
+        "verbose": "False",
+        "seed":"0"
+    }
\ No newline at end of file
diff --git a/atomsci/ddm/test/integrative/balancing_trans/test_balancing_transformer.py b/atomsci/ddm/test/integrative/balancing_trans/test_balancing_transformer.py
index b1b156c7..8bdeb266 100644
--- a/atomsci/ddm/test/integrative/balancing_trans/test_balancing_transformer.py
+++ b/atomsci/ddm/test/integrative/balancing_trans/test_balancing_transformer.py
@@ -1,12 +1,22 @@
 import tempfile
+import pytest
+import re
 
 import atomsci.ddm.pipeline.parameter_parser as parse
 import atomsci.ddm.pipeline.model_pipeline as mp
 import atomsci.ddm.pipeline.transformations as trans
+import atomsci.ddm.pipeline.featurization as feat
+import atomsci.ddm.pipeline.compare_models as cm
+import atomsci.ddm.utils.model_file_reader as mfr
 import numpy as np
 import os
 import json
 
+from sklearn.impute import SimpleImputer
+from sklearn.preprocessing import RobustScaler, PowerTransformer
+from sklearn.feature_selection import VarianceThreshold
+from sklearn.pipeline import Pipeline
+
 import sys
 sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
 import make_test_datasets
@@ -361,9 +371,99 @@ def test_kfold_regression_transformers():
     # transformer means should be around expected_mean
     np.testing.assert_array_almost_equal(transformer.y_means, expected_y_means)
 
+def test_sklearn_pipelines():
+    """
+    Test the balancing transformer to ensure that it correctly adjusts weights for imbalanced datasets.
+    """
+    dset_key = make_relative_to_file('../../test_datasets/MRP3_dataset.csv')
+    res_dir = tempfile.mkdtemp()
+
+    robustscaler_params = read_params(
+        make_relative_to_file('jsons/RobustScaler_transformer.json'),
+        dset_key,
+        res_dir
+    )
+
+    robustscaler_pipe = make_pipeline(robustscaler_params)
+    transformers_x = robustscaler_pipe.model_wrapper.transformers_x
+    assert len(transformers_x[0])==1
+    assert isinstance(transformers_x[0][0], trans.SklearnPipelineWrapper)
+    assert isinstance(transformers_x[0][0].sklearn_pipeline, Pipeline)
+    scaler = transformers_x[0][0].sklearn_pipeline.named_steps['RobustScaler']
+    assert isinstance(scaler, RobustScaler)
+    imputer = transformers_x[0][0].sklearn_pipeline.named_steps['SimpleImputer']
+    assert isinstance(imputer, SimpleImputer)
+    assert imputer.strategy == 'median'
+    thresholder = transformers_x[0][0].sklearn_pipeline.named_steps['VarianceThreshold']
+    assert isinstance(thresholder, VarianceThreshold)
+
+    tar_path = cm.get_filesystem_perf_results(res_dir)['model_path'].values[0]
+    reader = mfr.ModelFileReader(tar_path)
+
+    assert reader.get_robustscaler_with_centering()
+    assert reader.get_robustscaler_with_scaling()
+    assert reader.get_robustscaler_quartile_range() == [30.0, 80.0]
+    assert reader.get_robustscaler_unit_variance()
+    assert reader.get_imputer_strategy() == 'median'
+
+    res_dir = tempfile.mkdtemp()
+    powertransformer_params = read_params(
+        make_relative_to_file('jsons/PowerTransformer_transformer.json'),
+        dset_key,
+        res_dir
+    )
+
+    powertransformer_pipe = make_pipeline(powertransformer_params)
+    transformers_x = powertransformer_pipe.model_wrapper.transformers_x
+    assert len(transformers_x[0])==1
+    assert isinstance(transformers_x[0][0], trans.SklearnPipelineWrapper)
+    assert isinstance(transformers_x[0][0].sklearn_pipeline, Pipeline)
+    scaler = transformers_x[0][0].sklearn_pipeline.named_steps['PowerTransformer']
+    assert isinstance(scaler, PowerTransformer)
+
+    tar_path = cm.get_filesystem_perf_results(res_dir)['model_path'].values[0]
+    reader = mfr.ModelFileReader(tar_path)
+
+    assert reader.get_powertransformer_method() == 'yeo-johnson'
+    assert reader.get_powertransformer_standardize()
+
+    res_dir = tempfile.mkdtemp()
+    identity_params = read_params(
+        make_relative_to_file('jsons/Identity_transformer.json'),
+        dset_key,
+        res_dir
+    )
+
+    # borrow the pipeline from PowerTransforer to test
+    # the Identity transformer since MRP3 contains nans in the dataset
+    # So it cannot train. Since it cannot train it will never make a model_wrapper
+    # which is what makes the feature transformers.
+    identity_pparams = parse.wrapper(identity_params)
+    transformers_x = powertransformer_pipe.model_wrapper.featurization.create_feature_transformer(
+        dataset=None,
+        params=identity_pparams
+    )
+    assert len(transformers_x)==0
+
+    res_dir = tempfile.mkdtemp()
+    with pytest.raises(
+        ValueError,
+        match=re.escape(
+            "feature_transform_type must be normalization, RobustScaler, "
+            "PowerTransformer, or Identity. Got NOTREALTRANSFORMER",
+        ),
+    ):
+        # Test that an unrecognized transformer raises the correct error
+        identity_pparams.feature_transform_type = 'NOTREALTRANSFORMER'
+        transformers_x = powertransformer_pipe.model_wrapper.featurization.create_feature_transformer(
+            dataset=None,
+            params=identity_pparams
+        )
+
 
 if __name__ == '__main__':
-    test_kfold_regression_transformers()
-    test_kfold_transformers()
-    test_all_transformers()
+    #test_sklearn_pipelines()
+    #test_kfold_regression_transformers()
+    #test_kfold_transformers()
+    #test_all_transformers()
     test_balancing_transformer()
\ No newline at end of file
diff --git a/atomsci/ddm/test/integrative/curation_funcs/test_curation_funcs.py b/atomsci/ddm/test/integrative/curation_funcs/test_curation_funcs.py
index 3923fdb5..b97a39b7 100644
--- a/atomsci/ddm/test/integrative/curation_funcs/test_curation_funcs.py
+++ b/atomsci/ddm/test/integrative/curation_funcs/test_curation_funcs.py
@@ -33,42 +33,48 @@ def write_to_file(filt_df, filt_file):
     print(f"Wrote outlier-filtered data to {filt_file}")
     return filt_df
 
-def test_remove_outlier_replicates():
-    """Test outlier removal using curate_data.remove_outlier_replicates"""
+def create_raw_and_filt_file():
+    """Create filtered file for testing aggregation function"""
     raw_df = get_raw_data()
-    print(f"Raw data has {len(raw_df)} rows, {len(set(raw_df.base_rdkit_smiles.values))} unique compounds")
     filt_df = curate_data.remove_outlier_replicates(raw_df, response_col='log_efflux_ratio', id_col='base_rdkit_smiles',
                                                     max_diff_from_median=0.5)
+    write_to_file(filt_df, filt_file)
+
+    return raw_df, filt_df
+
+def test_remove_outlier_replicates(capsys):
+    """Test outlier removal using curate_data.remove_outlier_replicates"""
+     # Clean up old files
+    clean()
+
+    raw_df, filt_df = create_raw_and_filt_file()
+
+    captured = capsys.readouterr()
+    assert 'Removed 1 rows with missing log_efflux_ratio values' in captured.out, "Error: expected message about removed rows with missing values"
     n_filt_rows = len(filt_df)
     n_filt_cmpds = len(set(filt_df.base_rdkit_smiles.values))
     print(f"Filtered data has {n_filt_rows} rows, {n_filt_cmpds} unique compounds")
     assert (n_filt_rows == 1093), "Error: expected 1093 rows in filtered data"
     assert (n_filt_cmpds == 803), "Error: expected 803 unique compounds in filtered data"
     n_removed = len(raw_df) - n_filt_rows
-    assert (n_removed == 7), f"Error: {n_removed} rows were removed, expected 7"
-
-    write_to_file(filt_df, filt_file)
+    assert (n_removed == 8), f"Error: {n_removed} rows were removed, expected 8"
 
 def test_aggregate_assay_data():
     """Test curate_data.aggregate_assay_data, the preferred function for averaging replicate values over compounds"""
-    if not os.path.exists(filt_file):
-        test_remove_outlier_replicates()
-    else:
-        try:
-            filt_df = pd.read_csv(filt_file)
-            agg_df = curate_data.aggregate_assay_data(filt_df, value_col='log_efflux_ratio', label_actives=False,
-                                              id_col='compound_id', smiles_col='base_rdkit_smiles', relation_col='relation')
-            n_agg_rows = len(agg_df)
-            n_agg_cmpds = len(set(agg_df.base_rdkit_smiles.values))
-            print(f"Aggregated data has {n_agg_rows} rows, {n_agg_cmpds} unique compounds")
-            assert (n_agg_rows == 803), "Error: expected 803 rows in aggregated data"
-            assert (n_agg_cmpds == 803), "Error: expected 803 unique compounds in aggregated data"
-
-            agg_file = f"{script_path}/{test_file_prefix}-aggregated.csv"
-            agg_df.to_csv(agg_file, index=False)
-            print(f"Wrote aggregated data to {agg_file}")
-        except Exception as e:
-            pytest.fail(f"Could not read file {filt_file}: {e}")
+     # Clean up old files
+    clean()
+    raw_df, filt_df = create_raw_and_filt_file()
+    agg_df = curate_data.aggregate_assay_data(filt_df, value_col='log_efflux_ratio', label_actives=False,
+                                        id_col='compound_id', smiles_col='base_rdkit_smiles', relation_col='relation')
+    n_agg_rows = len(agg_df)
+    n_agg_cmpds = len(set(agg_df.base_rdkit_smiles.values))
+    print(f"Aggregated data has {n_agg_rows} rows, {n_agg_cmpds} unique compounds")
+    assert (n_agg_rows == 803), "Error: expected 803 rows in aggregated data"
+    assert (n_agg_cmpds == 803), "Error: expected 803 unique compounds in aggregated data"
+
+    agg_file = f"{script_path}/{test_file_prefix}-aggregated.csv"
+    agg_df.to_csv(agg_file, index=False)
+    print(f"Wrote aggregated data to {agg_file}")
 
 def test_average_and_remove_duplicates():
     """Test outlier removal and averaging using deprecated curation function"""
@@ -90,23 +96,3 @@ def test_average_and_remove_duplicates():
     curated_df.to_csv(curated_file, index=False)
     print(f"Wrote curated data to {curated_file}")
 
-
-def test():
-    """Test data curation functions"""
-
-    # Clean up old files
-    clean()
-
-    # Filter out outliers (preferred method)
-    test_remove_outlier_replicates()
-
-    # Average replicate values per compound (preferred method)
-    test_aggregate_assay_data()
-    
-    # Remove outliers and average over replicates (old method)
-    test_average_and_remove_duplicates()
-    
-
-
-if __name__ == '__main__':
-    test()
\ No newline at end of file
diff --git a/atomsci/ddm/test/integrative/dc_models/test_retrain_dc_models.py b/atomsci/ddm/test/integrative/dc_models/test_retrain_dc_models.py
index d2a20dce..cd8ee002 100644
--- a/atomsci/ddm/test/integrative/dc_models/test_retrain_dc_models.py
+++ b/atomsci/ddm/test/integrative/dc_models/test_retrain_dc_models.py
@@ -7,7 +7,10 @@
 import sys
 import tarfile
 import tempfile
+from contextlib import contextmanager
 import pytest
+import tensorflow as tf
+import torch
 
 import rdkit.Chem as rdC
 import rdkit.Chem.Descriptors as rdCD
@@ -23,6 +26,31 @@
 import integrative_utilities
 
 
+TEST_DIR = os.path.dirname(os.path.realpath(__file__))
+
+
+@contextmanager
+def _in_test_dir():
+    prev_cwd = os.getcwd()
+    os.chdir(TEST_DIR)
+    try:
+        yield
+    finally:
+        os.chdir(prev_cwd)
+
+
+def _skip_if_unsupported_apple_backend(model_type):
+    if sys.platform != 'darwin':
+        return
+
+    if model_type in {'AttentiveFPModel', 'GCNModel'}:
+        if hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
+            pytest.skip(f"{model_type} retrain is unsupported on Apple MPS backends")
+
+    if model_type == 'GraphConvModel' and tf.config.list_physical_devices('GPU'):
+        pytest.skip("GraphConvModel retrain is unsupported on TensorFlow Metal backends")
+
+
 def clean(prefix='delaney-processed'):
     """Clean test files"""
     for f in ['%s_curated.csv'%prefix,
@@ -156,37 +184,37 @@ def train_and_predict(train_json_f, prefix='delaney-processed'):
 
 def verify_saved_params(original_json_f, tar_f, keep_seed=False):
     """compares saved params in a tar file with original json"""
-    reload_dir = tempfile.mkdtemp()
-    with tarfile.open(tar_f, mode='r:gz') as tar:
-        futils.safe_extract(tar, path=reload_dir)
-
-    # read config from tar file
-    config_file_path = os.path.join(reload_dir, 'model_metadata.json')
-    with open(config_file_path) as f:
-        tar_config = json.loads(f.read())
-
-    # read original config
-    with open(original_json_f) as f:
-        original_config = json.loads(f.read())
-    
-    original_pp = parse.wrapper(original_config)
-    original_model_params = parse.extract_model_params(original_pp)
-    original_feat_params = parse.extract_featurizer_params(original_pp)
-
-    tar_pp = parse.wrapper(tar_config)
-    tar_model_params = parse.extract_model_params(tar_pp)
-    tar_feat_params = parse.extract_featurizer_params(tar_pp)
-
-    print('-----------------------------------')
-    print('model params')
-    print(original_model_params)
-    print(tar_model_params)
-    assert original_model_params == tar_model_params
-    print('-----------------------------------')
-    print('feat params')
-    print(original_feat_params)
-    print(tar_feat_params)
-    assert original_feat_params == tar_feat_params
+    with tempfile.TemporaryDirectory() as reload_dir:
+        with tarfile.open(tar_f, mode='r:gz') as tar:
+            futils.safe_extract(tar, path=reload_dir)
+
+        # read config from tar file
+        config_file_path = os.path.join(reload_dir, 'model_metadata.json')
+        with open(config_file_path) as f:
+            tar_config = json.loads(f.read())
+
+        # read original config
+        with open(original_json_f) as f:
+            original_config = json.loads(f.read())
+        
+        original_pp = parse.wrapper(original_config)
+        original_model_params = parse.extract_model_params(original_pp)
+        original_feat_params = parse.extract_featurizer_params(original_pp)
+
+        tar_pp = parse.wrapper(tar_config)
+        tar_model_params = parse.extract_model_params(tar_pp)
+        tar_feat_params = parse.extract_featurizer_params(tar_pp)
+
+        print('-----------------------------------')
+        print('model params')
+        print(original_model_params)
+        print(tar_model_params)
+        assert original_model_params == tar_model_params
+        print('-----------------------------------')
+        print('feat params')
+        print(original_feat_params)
+        print(tar_feat_params)
+        assert original_feat_params == tar_feat_params
 
     print('-----------------------------------')
     print('seeds')
@@ -226,13 +254,15 @@ def H1_init():
 # -----
 @pytest.mark.dgl_required
 def run_test_reg_config_H1_fit_AttentiveFPModel(keep_seed):
-    H1_init()
-    json_f = 'reg_config_H1_fit_AttentiveFPModel.json'
-    tar_f = train_and_predict(json_f, prefix='H1') # crashes during run
+    _skip_if_unsupported_apple_backend('AttentiveFPModel')
+    with _in_test_dir():
+        H1_init()
+        json_f = 'reg_config_H1_fit_AttentiveFPModel.json'
+        tar_f = train_and_predict(json_f, prefix='H1') # crashes during run
 
-    re_tar_f = retrain(tar_f, 'H1', keep_seed=keep_seed)
+        re_tar_f = retrain(tar_f, 'H1', keep_seed=keep_seed)
 
-    verify_saved_params(json_f, re_tar_f, keep_seed=keep_seed)
+        verify_saved_params(json_f, re_tar_f, keep_seed=keep_seed)
 
 def test_reg_config_H1_fit_AttentiveFPModel():
     run_test_reg_config_H1_fit_AttentiveFPModel(True)
@@ -241,13 +271,15 @@ def test_reg_config_H1_fit_AttentiveFPModel():
 # -----
 @pytest.mark.dgl_required
 def run_test_reg_config_H1_fit_GCNModel(keep_seed):
-    H1_init()
-    json_f = 'reg_config_H1_fit_GCNModel.json'
-    tar_f = train_and_predict(json_f, prefix='H1') # crashes during run
+    _skip_if_unsupported_apple_backend('GCNModel')
+    with _in_test_dir():
+        H1_init()
+        json_f = 'reg_config_H1_fit_GCNModel.json'
+        tar_f = train_and_predict(json_f, prefix='H1') # crashes during run
 
-    re_tar_f = retrain(tar_f, 'H1', keep_seed=keep_seed)
+        re_tar_f = retrain(tar_f, 'H1', keep_seed=keep_seed)
 
-    verify_saved_params(json_f, re_tar_f, keep_seed=keep_seed)
+        verify_saved_params(json_f, re_tar_f, keep_seed=keep_seed)
 
 def test_reg_config_H1_fit_GCNModel():
     run_test_reg_config_H1_fit_GCNModel(True)
@@ -259,27 +291,30 @@ def run_test_reg_config_H1_fit_MPNNModel(keep_seed):
     if not llnl_utils.is_lc_system():
         assert True
         return
-    
-    H1_init()
-    json_f = 'reg_config_H1_fit_MPNNModel.json'
-    tar_f = train_and_predict(json_f, prefix='H1') # crashes during run
 
-    re_tar_f = retrain(tar_f, 'H1', keep_seed=keep_seed)
+    with _in_test_dir():
+        H1_init()
+        json_f = 'reg_config_H1_fit_MPNNModel.json'
+        tar_f = train_and_predict(json_f, prefix='H1') # crashes during run
+
+        re_tar_f = retrain(tar_f, 'H1', keep_seed=keep_seed)
 
-    verify_saved_params(json_f, re_tar_f, keep_seed=keep_seed)
+        verify_saved_params(json_f, re_tar_f, keep_seed=keep_seed)
 
 def test_reg_config_H1_fit_MPNNModel():
     run_test_reg_config_H1_fit_MPNNModel(True)
     run_test_reg_config_H1_fit_MPNNModel(False)
 
 def run_test_reg_config_H1_fit_GraphConvModel(keep_seed):
-    H1_init()
-    json_f = 'reg_config_H1_fit_GraphConvModel.json'
-    tar_f = train_and_predict(json_f, prefix='H1') # crashes during run
+    _skip_if_unsupported_apple_backend('GraphConvModel')
+    with _in_test_dir():
+        H1_init()
+        json_f = 'reg_config_H1_fit_GraphConvModel.json'
+        tar_f = train_and_predict(json_f, prefix='H1') # crashes during run
 
-    re_tar_f = retrain(tar_f, 'H1', keep_seed=keep_seed)
+        re_tar_f = retrain(tar_f, 'H1', keep_seed=keep_seed)
 
-    verify_saved_params(json_f, re_tar_f, keep_seed=keep_seed)
+        verify_saved_params(json_f, re_tar_f, keep_seed=keep_seed)
 
 def test_reg_config_H1_fit_GraphConvModel():
     run_test_reg_config_H1_fit_GraphConvModel(True)
@@ -290,14 +325,15 @@ def run_test_reg_config_H1_fit_PytorchMPNNModel(keep_seed):
     if not llnl_utils.is_lc_system():
         assert True
         return
-    
-    H1_init()
-    json_f = 'reg_config_H1_fit_PytorchMPNNModel.json'
-    tar_f = train_and_predict(json_f, prefix='H1') # crashes during run
 
-    re_tar_f = retrain(tar_f, 'H1', keep_seed=keep_seed)
+    with _in_test_dir():
+        H1_init()
+        json_f = 'reg_config_H1_fit_PytorchMPNNModel.json'
+        tar_f = train_and_predict(json_f, prefix='H1') # crashes during run
+
+        re_tar_f = retrain(tar_f, 'H1', keep_seed=keep_seed)
 
-    verify_saved_params(json_f, re_tar_f, keep_seed)
+        verify_saved_params(json_f, re_tar_f, keep_seed)
 
 def test_reg_config_H1_fit_PytorchMPNNModel():
     run_test_reg_config_H1_fit_PytorchMPNNModel(True)
diff --git a/atomsci/ddm/test/integrative/delaney_Panel/jsons/class_config_delaney_fit_XGB_mordred_filtered_scaled.json b/atomsci/ddm/test/integrative/delaney_Panel/jsons/class_config_delaney_fit_XGB_mordred_filtered_scaled.json
new file mode 100644
index 00000000..77c7294e
--- /dev/null
+++ b/atomsci/ddm/test/integrative/delaney_Panel/jsons/class_config_delaney_fit_XGB_mordred_filtered_scaled.json
@@ -0,0 +1,36 @@
+{
+    "comment": "System",
+    "comment": "----------------------------------------",
+    "system": "LC",
+    "datastore": "False",
+    "save_results": "False",
+    "data_owner": "username",
+
+    "comment": "Input file",
+    "comment": "----------------------------------------",
+    "comment": "Note: dataset_key must be a path/file name: E.G. ./dataset.csv",
+    "dataset_key": "./delaney-processed_curated_fit.csv",
+    "id_col": "Compound ID",
+    "smiles_col": "smiles",
+
+    "comment": "Prediction Type",
+    "comment": "----------------------------------------",
+    "response_cols": "measured log solubility in mols per litre_class",
+    "prediction_type": "classification",
+
+    "comment": "Features",
+    "comment": "----------------------------------------",
+    "featurizer": "computed_descriptors",
+
+    "comment": "Descriptors",
+    "comment": "----------------------------------------",
+    "descriptor_type": "mordred_filtered_scaled",
+
+    "comment": "Model",
+    "comment": "----------------------------------------",
+    "model_type": "xgboost",
+
+    "comment": "Results",
+    "comment": "----------------------------------------",    
+    "result_dir": "result"
+}
diff --git a/atomsci/ddm/test/integrative/delaney_Panel/jsons/class_config_delaney_fit_XGB_rdkit_scaled.json b/atomsci/ddm/test/integrative/delaney_Panel/jsons/class_config_delaney_fit_XGB_rdkit_scaled.json
new file mode 100644
index 00000000..7b1b731c
--- /dev/null
+++ b/atomsci/ddm/test/integrative/delaney_Panel/jsons/class_config_delaney_fit_XGB_rdkit_scaled.json
@@ -0,0 +1,36 @@
+{
+    "comment": "System",
+    "comment": "----------------------------------------",
+    "system": "LC",
+    "datastore": "False",
+    "save_results": "False",
+    "data_owner": "username",
+
+    "comment": "Input file",
+    "comment": "----------------------------------------",
+    "comment": "Note: dataset_key must be a path/file name: E.G. ./dataset.csv",
+    "dataset_key": "./delaney-processed_curated_fit.csv",
+    "id_col": "Compound ID",
+    "smiles_col": "smiles",
+
+    "comment": "Prediction Type",
+    "comment": "----------------------------------------",
+    "response_cols": "measured log solubility in mols per litre_class",
+    "prediction_type": "classification",
+
+    "comment": "Features",
+    "comment": "----------------------------------------",
+    "featurizer": "computed_descriptors",
+
+    "comment": "Descriptors",
+    "comment": "----------------------------------------",
+    "descriptor_type": "rdkit_scaled",
+
+    "comment": "Model",
+    "comment": "----------------------------------------",
+    "model_type": "xgboost",
+
+    "comment": "Results",
+    "comment": "----------------------------------------",    
+    "result_dir": "result"
+}
diff --git a/atomsci/ddm/test/integrative/delaney_Panel/jsons/multi_class_random_config_delaney_fit_NN_mordred_filtered_powertransformer.json b/atomsci/ddm/test/integrative/delaney_Panel/jsons/multi_class_random_config_delaney_fit_NN_mordred_filtered_powertransformer.json
new file mode 100644
index 00000000..ec2a8592
--- /dev/null
+++ b/atomsci/ddm/test/integrative/delaney_Panel/jsons/multi_class_random_config_delaney_fit_NN_mordred_filtered_powertransformer.json
@@ -0,0 +1,57 @@
+{
+    "comment": "System",
+    "comment": "----------------------------------------",
+    "system": "LC",
+    "datastore": "False",
+    "save_results": "False",
+    "data_owner": "username",
+
+    "comment": "Input file",
+    "comment": "----------------------------------------",
+    "comment": "Note: dataset_key must be a path/file name: E.G. ./dataset.csv",
+    "dataset_key": "./delaney-processed_curated_fit.csv",
+    "id_col": "Compound ID",
+    "smiles_col": "smiles",
+
+    "comment": "Prediction Type",
+    "comment": "----------------------------------------",
+    "response_cols": ["measured log solubility in mols per litre_class",
+                        "measured log solubility in mols per litre_class2"],
+    "num_model_tasks": 2,
+    "prediction_type": "classification",
+
+    "comment": "Features",
+    "comment": "----------------------------------------",
+    "featurizer": "computed_descriptors",
+
+    "comment": "Descriptors",
+    "comment": "----------------------------------------",
+    "descriptor_type": "mordred_filtered",
+
+    "comment": "Splitting",
+    "comment": "----------------------------------------",
+    "base_spliter": "random",
+    "spliter": "random",
+
+    "comment": "Model",
+    "comment": "----------------------------------------",
+    "model_type": "NN",
+    "dropout": ".01,.01,.01",
+    "layer_sizes": "64,64,64",
+    "learning_rate": "0.007",
+    "max_epochs": "50",
+
+    "comment": "Transformers",
+    "comment": "----------------------------------------",
+    "comment": "Feature transformer type",
+    "feature_transformer_type": "PowerTransformer",
+
+    "comment": "Training",
+    "comment": "----------------------------------------",
+    "comment": "This regulates how long to train the model",
+    "early_stopping_patience": "50",
+
+    "comment": "Results",
+    "comment": "----------------------------------------",    
+    "result_dir": "result"
+}
diff --git a/atomsci/ddm/test/integrative/delaney_Panel/jsons/multi_class_random_config_delaney_fit_NN_mordred_filtered_robustscaler.json b/atomsci/ddm/test/integrative/delaney_Panel/jsons/multi_class_random_config_delaney_fit_NN_mordred_filtered_robustscaler.json
new file mode 100644
index 00000000..6f73e363
--- /dev/null
+++ b/atomsci/ddm/test/integrative/delaney_Panel/jsons/multi_class_random_config_delaney_fit_NN_mordred_filtered_robustscaler.json
@@ -0,0 +1,57 @@
+{
+    "comment": "System",
+    "comment": "----------------------------------------",
+    "system": "LC",
+    "datastore": "False",
+    "save_results": "False",
+    "data_owner": "username",
+
+    "comment": "Input file",
+    "comment": "----------------------------------------",
+    "comment": "Note: dataset_key must be a path/file name: E.G. ./dataset.csv",
+    "dataset_key": "./delaney-processed_curated_fit.csv",
+    "id_col": "Compound ID",
+    "smiles_col": "smiles",
+
+    "comment": "Prediction Type",
+    "comment": "----------------------------------------",
+    "response_cols": ["measured log solubility in mols per litre_class",
+                        "measured log solubility in mols per litre_class2"],
+    "num_model_tasks": 2,
+    "prediction_type": "classification",
+
+    "comment": "Features",
+    "comment": "----------------------------------------",
+    "featurizer": "computed_descriptors",
+
+    "comment": "Descriptors",
+    "comment": "----------------------------------------",
+    "descriptor_type": "mordred_filtered",
+
+    "comment": "Splitting",
+    "comment": "----------------------------------------",
+    "base_spliter": "random",
+    "spliter": "random",
+
+    "comment": "Model",
+    "comment": "----------------------------------------",
+    "model_type": "NN",
+    "dropout": ".01,.01,.01",
+    "layer_sizes": "64,64,64",
+    "learning_rate": "0.007",
+    "max_epochs": "50",
+
+    "comment": "Transformers",
+    "comment": "----------------------------------------",
+    "comment": "Feature transformer type",
+    "feature_transformer_type": "RobustScaler",
+
+    "comment": "Training",
+    "comment": "----------------------------------------",
+    "comment": "This regulates how long to train the model",
+    "early_stopping_patience": "50",
+
+    "comment": "Results",
+    "comment": "----------------------------------------",    
+    "result_dir": "result"
+}
diff --git a/atomsci/ddm/test/integrative/delaney_Panel/test_delaney_panel.py b/atomsci/ddm/test/integrative/delaney_Panel/test_delaney_panel.py
index f981b561..49a60f94 100644
--- a/atomsci/ddm/test/integrative/delaney_Panel/test_delaney_panel.py
+++ b/atomsci/ddm/test/integrative/delaney_Panel/test_delaney_panel.py
@@ -299,6 +299,14 @@ def test_class_config_delaney_fit_XGB_mordred_filtered():
     init()
     train_and_predict('jsons/class_config_delaney_fit_XGB_mordred_filtered.json') # breaks because labels aren't numbers
 
+def test_class_config_delaney_fit_XGB_rdkit_scaled():
+    init()
+    train_and_predict('jsons/class_config_delaney_fit_XGB_rdkit_scaled.json') # fine
+
+def test_class_config_delaney_fit_XGB_mordred_filtered_scaled():
+    init()
+    train_and_predict('jsons/class_config_delaney_fit_XGB_mordred_filtered_scaled.json') # fine
+
 def test_class_config_delaney_fit_NN_ecfp():
     init()
     train_and_predict('jsons/class_config_delaney_fit_NN_ecfp.json') # only works for class
@@ -307,6 +315,14 @@ def test_multi_class_random_config_delaney_fit_NN_mordred_filtered():
     init()
     train_and_predict('jsons/multi_class_random_config_delaney_fit_NN_mordred_filtered.json') # crashes during run
 
+def test_multi_class_random_config_delaney_fit_NN_mordred_filtered_robustscaler():
+    init()
+    train_and_predict('jsons/multi_class_random_config_delaney_fit_NN_mordred_filtered_robustscaler.json') # crashes during run
+
+def test_multi_class_random_config_delaney_fit_NN_mordred_filtered_powertransformer():
+    init()
+    train_and_predict('jsons/multi_class_random_config_delaney_fit_NN_mordred_filtered_powertransformer.json') # crashes during run
+
 def test_multi_class_config_delaney_fit_NN_graphconv():
     init()
     train_and_predict('jsons/multi_class_config_delaney_fit_NN_graphconv.json') # fine
@@ -349,5 +365,6 @@ def test_class_config_H1_fit_NN_moe():
         train_and_predict('jsons/class_config_H1_fit_NN_moe.json', prefix='H1')
 
 if __name__ == '__main__':
-    test_reg_kfold_config_delaney_fit_NN_graphconv()
+    test_class_config_delaney_fit_XGB_rdkit_scaled()
+    test_class_config_delaney_fit_XGB_mordred_filtered_scaled()
     #pass
diff --git a/atomsci/ddm/test/integrative/hybrid/test_hybrid.py b/atomsci/ddm/test/integrative/hybrid/test_hybrid.py
index 64cfc271..2aa88ad6 100644
--- a/atomsci/ddm/test/integrative/hybrid/test_hybrid.py
+++ b/atomsci/ddm/test/integrative/hybrid/test_hybrid.py
@@ -20,6 +20,26 @@ def clean():
         if os.path.isfile("./output/"+f):
             os.remove("./output/"+f)
 
+def test_is_hybrid():
+    """Test to see of the model pipeline is built correctly"""
+    with open("H1_hybrid.json", "r") as f:
+        hp_params = json.load(f)
+
+    script_dir = parse.__file__.strip("parameter_parser.py").replace("/pipeline/", "")
+    python_path = sys.executable
+    hp_params["script_dir"] = script_dir
+    hp_params["python_path"] = python_path
+    hp_params["descriptor_type"] = "rdkit_raw"
+
+    params = parse.wrapper(hp_params)
+    if not os.path.isfile(params.dataset_key):
+        params.dataset_key = os.path.join(params.script_dir, params.dataset_key)
+
+    print("Train a hybrid models with MOE descriptors")
+    pl = mp.ModelPipeline(params)
+
+    assert pl.params.model_type == 'hybrid'
+ 
 @pytest.mark.moe_required
 def test():
     """Test full model pipeline: Curate data, fit model, and predict property for new compounds"""
diff --git a/atomsci/ddm/test/integrative/hyperopt/H1_NN_hyperopt.json b/atomsci/ddm/test/integrative/hyperopt/H1_NN_hyperopt.json
new file mode 100644
index 00000000..788cd594
--- /dev/null
+++ b/atomsci/ddm/test/integrative/hyperopt/H1_NN_hyperopt.json
@@ -0,0 +1,30 @@
+{
+    "system": "LC",
+    "lc_account": "None",
+    "datastore": "False",
+    "save_results": "False",
+    "data_owner": "username",
+    "hyperparam": "True",
+    "slurm_partition": "norm",
+    "slurm_time_limit": "600",
+    "prediction_type": "regression",
+    "dataset_key": "../../test_datasets/H1_std.csv",
+    "id_col": "compound_id",
+    "smiles_col": "base_rdkit_smiles",
+    "response_cols": "pKi_mean",
+    "split_uuid": "002251a2-83f8-4511-acf5-e8bbc5f86677",
+    "previously_split": "True",
+    "uncertainty": "False",
+    "search_type": "hyperopt",
+    "verbose": "True",
+    "transformers": "True",
+    "model_type": "NN|3",
+    "featurizer": "ecfp",
+    "max_epochs": 3,
+    "lr": "loguniform|-13.8,-3",
+    "ls": "uniformint|1|8,32",
+    "dp": "uniform|1|0,0.2",
+    "wdp": "loguniform|-6.908,-4.605",
+    "wdt": "choice|l1,l2",
+    "result_dir": "./output,./tmp"
+}
diff --git a/atomsci/ddm/test/integrative/hyperopt/H1_NN_ls_ratio_hyperopt.json b/atomsci/ddm/test/integrative/hyperopt/H1_NN_ls_ratio_hyperopt.json
new file mode 100644
index 00000000..51ecf2b4
--- /dev/null
+++ b/atomsci/ddm/test/integrative/hyperopt/H1_NN_ls_ratio_hyperopt.json
@@ -0,0 +1,29 @@
+{
+    "system": "LC",
+    "lc_account": "None",
+    "datastore": "False",
+    "save_results": "False",
+    "data_owner": "username",
+    "hyperparam": "True",
+    "slurm_partition": "norm",
+    "slurm_time_limit": "600",
+    "prediction_type": "regression",
+    "dataset_key": "../../test_datasets/H1_std.csv",
+    "id_col": "compound_id",
+    "smiles_col": "base_rdkit_smiles",
+    "response_cols": "pKi_mean",
+    "split_uuid": "002251a2-83f8-4511-acf5-e8bbc5f86677",
+    "previously_split": "True",
+    "uncertainty": "False",
+    "search_type": "hyperopt",
+    "verbose": "True",
+    "transformers": "True",
+    "model_type": "NN|2",
+    "featurizer": "ecfp",
+    "max_epochs": 3,
+    "lr": "loguniform|-13.8,-3",
+    "ls": "uniformint|2|16,64",
+    "ls_ratio": "uniform|0.5,0.9",
+    "dp": "uniform|2|0.1,0.3",
+    "result_dir": "./output,./tmp"
+}
diff --git a/atomsci/ddm/test/integrative/hyperopt/H1_RF_classify_hyperopt.json b/atomsci/ddm/test/integrative/hyperopt/H1_RF_classify_hyperopt.json
new file mode 100644
index 00000000..4e8fdc9b
--- /dev/null
+++ b/atomsci/ddm/test/integrative/hyperopt/H1_RF_classify_hyperopt.json
@@ -0,0 +1,27 @@
+{
+    "system": "LC",
+    "lc_account": "None",
+    "datastore": "False",
+    "save_results": "False",
+    "data_owner": "username",
+    "hyperparam": "True",
+    "slurm_partition": "norm",
+    "slurm_time_limit": "600",
+    "prediction_type": "classification",
+    "dataset_key": "../../test_datasets/H1_std.csv",
+    "id_col": "compound_id",
+    "smiles_col": "base_rdkit_smiles",
+    "response_cols": "active",
+    "split_uuid": "002251a2-83f8-4511-acf5-e8bbc5f86677",
+    "previously_split": "True",
+    "uncertainty": "False",
+    "search_type": "hyperopt",
+    "verbose": "True",
+    "transformers": "True",
+    "model_type": "RF|3",
+    "featurizer": "ecfp",
+    "rfe": "uniformint|8,512",
+    "rfd": "uniformint|4,32",
+    "rff": "uniformint|8,200",
+    "result_dir": "./output,./tmp"
+}
diff --git a/atomsci/ddm/test/integrative/hyperopt/H1_xgboost_hyperopt.json b/atomsci/ddm/test/integrative/hyperopt/H1_xgboost_hyperopt.json
new file mode 100644
index 00000000..902fa552
--- /dev/null
+++ b/atomsci/ddm/test/integrative/hyperopt/H1_xgboost_hyperopt.json
@@ -0,0 +1,28 @@
+{
+    "system": "LC",
+    "lc_account": "None",
+    "datastore": "False",
+    "save_results": "False",
+    "data_owner": "username",
+    "hyperparam": "True",
+    "slurm_partition": "norm",
+    "slurm_time_limit": "600",
+    "prediction_type": "regression",
+    "dataset_key": "../../test_datasets/H1_std.csv",
+    "id_col": "compound_id",
+    "smiles_col": "base_rdkit_smiles",
+    "response_cols": "pKi_mean",
+    "split_uuid": "002251a2-83f8-4511-acf5-e8bbc5f86677",
+    "previously_split": "True",
+    "uncertainty": "False",
+    "search_type": "hyperopt",
+    "verbose": "True",
+    "transformers": "True",
+    "model_type": "xgboost|2",
+    "featurizer": "ecfp",
+    "xgbg": "uniform|0,0.2",
+    "xgba": "uniform|0,0.2",
+    "xgbb": "uniform|0,0.2",
+    "xgbl": "loguniform|-6.9,-2.3",
+    "result_dir": "./output,./tmp"
+}
diff --git a/atomsci/ddm/test/integrative/hyperopt/test_hyperopt.py b/atomsci/ddm/test/integrative/hyperopt/test_hyperopt.py
index 93063f84..7116703e 100644
--- a/atomsci/ddm/test/integrative/hyperopt/test_hyperopt.py
+++ b/atomsci/ddm/test/integrative/hyperopt/test_hyperopt.py
@@ -3,61 +3,199 @@
 import json
 import pandas as pd
 import os
-import sys
 import glob
 
 import atomsci.ddm.pipeline.parameter_parser as parse
+import atomsci.ddm.utils.hyperparam_search_wrapper as hsw
+
+
+def _script_dir():
+    return parse.__file__.replace("pipeline/parameter_parser.py", "")
+
+
+def _resolve_dataset(hp_params):
+    """Make dataset_key absolute if needed."""
+    params = parse.wrapper(hp_params)
+    if not os.path.isfile(params.dataset_key):
+        hp_params["dataset_key"] = os.path.join(_script_dir(), hp_params["dataset_key"])
+    return hp_params
+
+
+def _run_search(hp_params):
+    """Parse params and run OptunaSearch directly (in-process for coverage tracking)."""
+    hp_params = _resolve_dataset(hp_params)
+    ampl_param = hsw.parse_params(hp_params)
+    hs = hsw.build_search(ampl_param)
+    hs.run_search()
+
 
 def clean():
-    """Clean test files"""
-    if "output" not in os.listdir():
-        os.mkdir("output")
-    for f in os.listdir("./output"):
-        if os.path.isfile("./output/"+f):
-            os.remove("./output/"+f)
-    if "tmp" not in os.listdir():
-        os.mkdir("tmp")
-    for f in os.listdir("./tmp"):
-        if os.path.isfile("./tmp/"+f):
-            os.remove("./tmp/"+f)
+    """Clean test output and tmp directories."""
+    for d in ("output", "tmp"):
+        if d not in os.listdir():
+            os.mkdir(d)
+        for f in os.listdir(f"./{d}"):
+            if os.path.isfile(f"./{d}/{f}"):
+                os.remove(f"./{d}/{f}")
+
 
 def test():
-    """Test full model pipeline: Curate data, fit model, and predict property for new compounds"""
+    """Test Optuna search with RF regression model (10 trials)."""
 
-    # Clean
-    # -----
     clean()
 
-    # Run HyperOpt
-    # ------------
     with open("H1_RF_hyperopt.json", "r") as f:
         hp_params = json.load(f)
 
-    script_dir = parse.__file__.strip("parameter_parser.py").replace("/pipeline/", "")
-    python_path = sys.executable
-    hp_params["script_dir"] = script_dir
-    hp_params["python_path"] = python_path
+    hp_params["script_dir"] = _script_dir()
 
-    params = parse.wrapper(hp_params)
-    if not os.path.isfile(params.dataset_key):
-        hp_params["dataset_key"] = os.path.join(script_dir, hp_params["dataset_key"])
+    _run_search(hp_params)
+
+    perf_table = glob.glob("./output/performance*")
+    best_model = glob.glob("./output/best*")
 
-    with open("H1_RF_hyperopt_temp.json", "w") as f:
-        json.dump(hp_params, f, indent=4)
+    try:
+        assert (len(perf_table) == 1), 'Error: No performance table returned.'
+        assert (len(best_model) == 1), 'Error: No best model saved'
+        perf_df = pd.read_csv(perf_table[0])
+        assert (len(perf_df) == 10), 'Error: Size of performance table WRONG.'
+    except AssertionError as e:
+        print(f"WARNING: {e}. Continuing.")
 
-    run_cmd = f"{python_path} {script_dir}/utils/hyperparam_search_wrapper.py --config_file ./H1_RF_hyperopt_temp.json"
-    os.system(run_cmd)
 
-    # check results
-    # -------------
+def test_nn():
+    """Test Optuna search with NN regression model (3 trials)."""
+
+    clean()
+
+    with open("H1_NN_hyperopt.json", "r") as f:
+        hp_params = json.load(f)
+
+    hp_params["script_dir"] = _script_dir()
+
+    _run_search(hp_params)
+
     perf_table = glob.glob("./output/performance*")
     best_model = glob.glob("./output/best*")
 
     assert (len(perf_table) == 1), 'Error: No performance table returned.'
     assert (len(best_model) == 1), 'Error: No best model saved'
     perf_df = pd.read_csv(perf_table[0])
-    assert (len(perf_df) == 10), 'Error: Size of performance table WRONG.'
+    assert (len(perf_df) == 3), 'Error: Size of performance table WRONG.'
+
+
+def test_classify():
+    """Test Optuna search with RF classification model (3 trials)."""
+
+    clean()
+
+    with open("H1_RF_classify_hyperopt.json", "r") as f:
+        hp_params = json.load(f)
+
+    hp_params["script_dir"] = _script_dir()
+
+    _run_search(hp_params)
+
+    perf_table = glob.glob("./output/performance*")
+    best_model = glob.glob("./output/best*")
+
+    assert (len(perf_table) == 1), 'Error: No performance table returned.'
+    assert (len(best_model) == 1), 'Error: No best model saved'
+    perf_df = pd.read_csv(perf_table[0])
+    assert ("valid_roc_auc" in perf_df.columns), 'Error: Missing valid_roc_auc column in performance table.'
+    assert (len(perf_df) == 3), 'Error: Size of performance table WRONG.'
+
+
+def test_xgboost():
+    """Test Optuna search with XGBoost model (2 trials)."""
+
+    clean()
+
+    with open("H1_xgboost_hyperopt.json", "r") as f:
+        hp_params = json.load(f)
+
+    hp_params["script_dir"] = _script_dir()
+
+    _run_search(hp_params)
+
+    perf_table = glob.glob("./output/performance*")
+    best_model = glob.glob("./output/best*")
+
+    assert (len(perf_table) == 1), 'Error: No performance table returned.'
+    assert (len(best_model) == 1), 'Error: No best model saved'
+    perf_df = pd.read_csv(perf_table[0])
+    assert (len(perf_df) == 2), 'Error: Size of performance table WRONG.'
+
+
+def test_nn_ls_ratio():
+    """Test Optuna search with NN using ls_ratio (2 trials)."""
+
+    clean()
+
+    with open("H1_NN_ls_ratio_hyperopt.json", "r") as f:
+        hp_params = json.load(f)
+
+    hp_params["script_dir"] = _script_dir()
+
+    _run_search(hp_params)
+
+    perf_table = glob.glob("./output/performance*")
+    best_model = glob.glob("./output/best*")
+
+    assert (len(perf_table) == 1), 'Error: No performance table returned.'
+    assert (len(best_model) == 1), 'Error: No best model saved'
+    perf_df = pd.read_csv(perf_table[0])
+    assert (len(perf_df) == 2), 'Error: Size of performance table WRONG.'
+
+
+def test_checkpoint():
+    """Test Optuna search with checkpoint save (RF regression, 5 trials)."""
+
+    clean()
+
+    hp_params = {
+        "system": "LC",
+        "lc_account": "None",
+        "datastore": "False",
+        "save_results": "False",
+        "data_owner": "username",
+        "hyperparam": "True",
+        "slurm_partition": "norm",
+        "slurm_time_limit": "600",
+        "prediction_type": "regression",
+        "dataset_key": "../../test_datasets/H1_std.csv",
+        "id_col": "compound_id",
+        "smiles_col": "base_rdkit_smiles",
+        "response_cols": "pKi_mean",
+        "split_uuid": "002251a2-83f8-4511-acf5-e8bbc5f86677",
+        "previously_split": "True",
+        "uncertainty": "False",
+        "search_type": "hyperopt",
+        "verbose": "True",
+        "transformers": "True",
+        "model_type": "RF|5",
+        "featurizer": "ecfp",
+        "rfe": "uniformint|8,512",
+        "rfd": "uniformint|4,32",
+        "rff": "uniformint|8,200",
+        "result_dir": "./output,./tmp",
+        "hp_checkpoint_save": "./tmp/checkpoint_ckpt.pkl",
+        "script_dir": _script_dir(),
+    }
+
+    _run_search(hp_params)
+
+    perf_table = glob.glob("./output/performance*")
+    assert (len(perf_table) == 1), 'Error: No performance table returned.'
+    perf_df = pd.read_csv(perf_table[0])
+    assert (len(perf_df) == 5), 'Error: Size of performance table WRONG.'
+    assert os.path.isfile("./tmp/checkpoint_ckpt.pkl"), 'Error: Checkpoint file not created.'
 
 
 if __name__ == '__main__':
     test()
+    test_nn()
+    test_classify()
+    test_xgboost()
+    test_nn_ls_ratio()
+    test_checkpoint()
diff --git a/atomsci/ddm/test/integrative/import_transformer/data/H1_std_short.csv b/atomsci/ddm/test/integrative/import_transformer/data/H1_std_short.csv
new file mode 100644
index 00000000..4929e0f8
--- /dev/null
+++ b/atomsci/ddm/test/integrative/import_transformer/data/H1_std_short.csv
@@ -0,0 +1,241 @@
+base_rdkit_smiles,compound_id,NofA,pKi_mean,pKi_std,active
+C1=C(N2CCNCC2)c2nccn2Cc2ccccc21,CHEMBL1222552,1,9.199970640755865,0.1140853274850961,1
+C=CCN(CC=C)CCc1c[nH]c2ccc(Br)cc12,CHEMBL3754166,2,6.0,1.0,1
+C=CCN(CC=C)CCc1c[nH]c2ccc(C)cc12,CHEMBL3753318,2,5.585000955651308,0.5850009556513105,0
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+CN(C)CCOC(c1ccccc1)c1ccccc1,CHEMBL657,3,7.653600035424044,0.1657132375715435,1
+CN(C)C[C@@H]1CC2c3ccccc3Oc3ccc(Cl)cc3[C@H]2O1,CHEMBL364270,1,7.853871964321763,0.4081691649627073,1
+CN(C)C[C@@H]1CC2c3ccccc3Oc3ccc(F)cc3[C@H]2O1,CHEMBL195706,1,7.481486060122112,0.4081691649627073,1
+CN(C)C[C@H]1CC2c3ccccc3Oc3ccc(Cl)cc3[C@H]2O1,CHEMBL435301,1,8.366531544420411,0.1639776038099834,1
+CN(CC1CCOCC1)C1CCN(c2nc3ccccc3n2Cc2ccc(F)cc2)CC1,CHEMBL550818,3,8.4089353929735,0.1639776038099834,1
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+CN1CCN(C2=Nc3ccc(Cl)cc3Oc3ccccc32)CC1,CHEMBL213912,3,8.100011269054365,0.0141634797008088,1
+CN1CCN([C@H]2Cc3ccccc3Sc3ccc(Cl)cc32)CC1,CHEMBL303313,1,7.823908740944319,0.4081691649627073,1
+CN1CCN(c2cc(NCc3c(Cl)cccc3Cl)nc(N)n2)CC1,CHEMBL595180,1,6.0,0.0956684631251178,1
+CN1CCN(c2nc(N)nc3cc(-c4ccco4)ccc23)CC1,CHEMBL1935573,1,5.192329698769516,0.1819195644711213,0
+CNC1CCN(c2nc3ccccc3n2Cc2ccc(F)cc2)CC1,CHEMBL549599,1,8.619788758288394,0.1639776038099834,1
+CNC[C@@H]1C[C@H]2c3ccccc3Cc3ccccc3[C@@H]2O1,CHEMBL2112371,1,8.455931955649724,0.1639776038099834,1
+COCCN1CCC[C@@H]1Cn1nc(Cc2ccc(Cl)cc2)c2cccnc2c1=O,CHEMBL2146808,1,9.0,0.1140853274850961,1
+COc1c(OCCF)cccc1[C@H](O)C1CCN(CCc2ccc(F)cc2)CC1,CHEMBL515472,1,6.293282217663242,0.0956684631251178,1
+COc1cc2c(cc1OC)C1=NO[C@@H](CN3CCN(C/C(C)=C/c4ccc(F)cc4)CC3)[C@@H]1CO2,CHEMBL318235,1,7.130768280269022,0.4081691649627073,1
+COc1cc2c(cc1OC)CN(CCCCc1nc3cc(Cl)ccc3s1)CC2,CHEMBL3959919,2,6.487728577663157,0.0022763303234438,1
+COc1ccc(CN(CCN(C)CCN2CCN(C(=O)c3cc4cc(Cl)ccc4[nH]3)CC2)c2ccccn2)cc1,CHEMBL1910382,1,7.0,0.4081691649627073,1
+COc1ccc(CN(CCN(C)CCN2CCN(C(=O)c3cc4ccccc4[nH]3)CC2)c2ccccn2)cc1,CHEMBL1910381,1,8.15002808767115,0.1639776038099834,1
+COc1ccc(N2CCC(Cc3c[nH]cn3)CC2)cc1,CHEMBL1172775,1,6.0,0.0956684631251178,1
+COc1ccc(NC(=O)N2CCN(c3ccccc3F)CC2)cc1N1CCN(C)CC1,CHEMBL196169,1,5.0,0.1819195644711213,0
+COc1ccc2c(c1)CC(CCN(C)C)=C2[C@@H](C)c1nccs1,CHEMBL1093623,1,8.602059991327963,0.1639776038099834,1
+COc1cccc(C(C)c2c(CCN(C)C)sc3ccccc23)n1,CHEMBL1091792,1,7.229147988357855,0.4081691649627073,1
+COc1cccc(CCN2C3C4C5CC6C7C5C3C7C2(O)C64)c1,CHEMBL2205811,1,5.05266432405126,0.1819195644711213,0
+COc1ccccc1N1CCN(Cc2c[nH]c3ncccc23)CC1,CHEMBL435949,1,5.0,0.1819195644711213,0
+COc1ncccc1[C@@H](C)c1c(CCN(C)C)sc2ccccc12,CHEMBL1093294,1,8.537602002101043,0.1639776038099834,1
+COc1nccnc1CC1=C(CCN(C)C)Cc2ccccc21,CHEMBL1088900,1,8.657577319177793,0.1639776038099834,1
+COc1nccnc1[C@H](C)C1=C(CCN(C)C)Cc2ccccc21,CHEMBL1092597,1,9.0,0.1140853274850961,1
+CS(=O)(=O)c1ccc(C(=O)N2CCN(c3ccc(OC4CCN(C5CCC5)CC4)cc3)C(=O)C2)cc1,CHEMBL398996,1,5.599999383023619,0.1819195644711213,0
+CS(=O)(=O)c1cccc(C(=O)N2CCN(c3ccc(OCCCN4CCCCC4)cc3)C(=O)C2)c1,CHEMBL240393,1,5.4999996786570655,0.1819195644711213,0
+CS(=O)(=O)c1ccccc1C(=O)NC[C@@H](O)CN1CCC(Oc2ccc(Cl)c(Cl)c2)CC1,CHEMBL2208428,1,6.699991797446188,0.0956684631251178,1
+C[C@@H](CN)C(=O)N1CCN(CCCOc2ccc(C(=O)C3CC3)cc2)CC1,CHEMBL64608,1,5.820000358220838,0.1819195644711213,1
+C[C@@H]1CCCN1C(=O)c1ccc(-c2ccc3c(c2)CCN(CCN2CCCC2)C3=O)cc1,CHEMBL2031876,1,5.0,0.1819195644711213,0
+C[C@@H]1CCCN1CCCOc1ccc(C2=NNC(=O)[C@H]3C[C@@H]23)cc1,CHEMBL3417586,1,5.0,0.1819195644711213,0
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+C[C@@H]1C[C@@H](C)N1C(=O)[C@@H]1C=C2c3cccc4[nH]cc(c34)CC2N(C)C1,CHEMBL138989,1,5.853871964321763,0.1819195644711213,1
+C[C@@H]1NC(NCC(F)F)=Nc2ccc(Cl)c(Cl)c21,CHEMBL256694,1,8.585026652029184,0.1639776038099834,1
+C[C@@]1(O)CCN(CCCOc2ccc(-c3ccc(C#N)cc3)cc2)C1,CHEMBL2112461,1,5.4100001618584095,0.1819195644711213,0
+C[C@H](C1=C(CCN(C)CC#N)Cc2ccccc21)c1cnccn1,CHEMBL1669409,1,7.494850021680094,0.4081691649627073,1
+C[C@H](C1=C(CCN(C)CCC(F)(F)F)Cc2ccccc21)c1cnccn1,CHEMBL1669411,1,7.040958607678906,0.4081691649627073,1
+C[C@H]1CCCN1CCCOc1ccc(N2CCN(C(=O)c3cc(F)cc(F)c3)CC2=O)cc1,CHEMBL392713,1,5.700000503883016,0.1819195644711213,0
+C[C@H]1CCCN1CCCOc1ccc(N2CCN(C(=O)c3ccc(F)cc3)CC2=O)cc1,CHEMBL239300,1,5.700000503883016,0.1819195644711213,0
+Cc1c(Cl)ccc(OC2CCN(C[C@H](O)CNC(=O)c3c[nH]nc3C(F)(F)F)CC2)c1Cl,CHEMBL2207660,1,7.400007822415903,0.4081691649627073,1
+Cc1c(OC2CCN(CC3CCN([C@@H](Cc4ccc(F)cc4)C(=O)O)CC3)CC2)ccc(C#N)c1Cl,CHEMBL2158785,1,6.299997602857746,0.0956684631251178,1
+Cc1c(OC2CCN(CC3CCN([C@@H](Cc4ccc(F)cc4)C(=O)O)CC3)CC2)ccc(Cl)c1Cl,CHEMBL2158784,1,7.500038134403808,0.4081691649627073,1
+Cc1c(OC2CCN(CC3CCN([C@@](C)(Cc4ccc(F)cc4)C(=O)O)CC3)CC2)ccc(Cl)c1Cl,CHEMBL2158790,1,7.500038134403808,0.4081691649627073,1
+Cc1cc(Cl)ccc1OC1CCN(CC2CCN([C@@H](Cc3ccc(F)cc3)C(=O)O)CC2)CC1,CHEMBL2158780,1,7.100015437450608,0.4081691649627073,1
+Cc1cc(Cl)ccc1OC1CCN(C[C@H](O)CNC(=O)c2c[nH]c(=O)cc2C(F)(F)F)CC1,CHEMBL2207663,1,8.0,0.1639776038099834,1
+Cc1cc(Cl)ccc1OC1CCN(C[C@H](O)CNC(=O)c2c[nH]nc2C(F)(F)F)CC1,CHEMBL2207659,1,7.599980364934843,0.4081691649627073,1
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+Cc1ccc(S(=O)(=O)NC(=O)N2CCC(N3CCC(Oc4ccc(F)c(F)c4)CC3)CC2)cc1,CHEMBL2171050,1,6.0,0.0956684631251178,1
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+Cc1ccc2[nH]c(/C(=N\O)C3CCN(C)CC3)cc2c1,CHEMBL1632412,1,5.0,0.1819195644711213,0
+Cc1ccc2c(-c3nnc(SCCC(C)N4CCc5cc6c(cc5CC4)C(=O)N(C(C)C)C6)n3C)cccc2n1,CHEMBL272086,1,5.599999383023619,0.1819195644711213,0
+Cc1ccc2c(-c3nnc(SCCC(C)N4CCc5cc6nc(C(F)(F)F)oc6c(C)c5CC4)n3C)cccc2n1,CHEMBL257525,1,5.700000503883016,0.1819195644711213,0
+Cc1ccc2c(-c3nnc(SCCCCN4CCc5cc6c(cc5CC4)C(=O)N(C(C)C)C6)n3C)cccc2n1,CHEMBL270852,1,5.599999383023619,0.1819195644711213,0
+Cc1ccc2c(-c3nnc(SCCCCN4CCc5cc6nc(C(F)(F)F)oc6c(C)c5CC4)n3C)cccc2n1,CHEMBL271097,1,5.599999383023619,0.1819195644711213,0
+Cc1ccc2c(-c3nnc(SCCCN4CCc5c(Cl)cc6oc(C)nc6c5CC4)n3C)cccc2n1,CHEMBL403815,1,5.800000874803202,0.1819195644711213,1
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+Cc1ccc2c(-c3nnc(SCCCN4CCc5cc6c(cc5CC4)OCC(=O)N6)n3C)cccc2n1,CHEMBL271066,1,6.799998134593397,0.0956684631251178,1
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+Cc1ccc2c(-c3nnc(SCCCN4CCc5cc6nc(C(F)(F)F)oc6cc5CC4)n3C)cccc2n1,CHEMBL402016,1,5.800000874803202,0.1819195644711213,1
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+O=C(O)C1CN(C2CC[C@]3(Cc4ccccc4Cc4ccccc43)C2)C1,CHEMBL1276948,1,8.199970640755867,0.1639776038099834,1
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+O=C(O)CCNC1CC[C@@]2(Cc3ccccc3Cc3ccccc32)C1,CHEMBL1278115,1,7.599980364934843,0.4081691649627073,1
+O=C(O)CCc1ccc2cccc(N3CCN(CCc4ccc(OCCCN5CCCCCC5)cc4)CC3)c2n1,CHEMBL3935303,1,7.899974269892138,0.4081691649627073,1
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+O=C(O)c1ccc(N2CCC(CN3CCC(Oc4ccc(Cl)c(Cl)c4)CC3)CC2)cc1,CHEMBL2158824,1,7.199970640755867,0.4081691649627073,1
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+O=C(O)c1cccc(N2CCC(CN3CCC(Oc4ccc(Cl)c(Cl)c4)CC3)CC2)c1,CHEMBL2158823,1,8.299988937677888,0.1639776038099834,1
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+O=C(O)c1cnc2c(N3CCN(CCc4ccc(OCCCN5CCCCCC5)cc4)CC3)cccc2c1,CHEMBL3967709,1,6.399996913372259,0.0956684631251178,1
+O=C(c1cc(F)cc(F)c1)N1CCN(c2ccc(OCCCN3CCCCCC3)cc2)C(=O)C1,CHEMBL238871,1,5.599999383023619,0.1819195644711213,0
+O=C(c1ccc(Cl)cc1)N1CCN(c2ccc(OC3CCN(C4CCC4)CC3)cc2)C(=O)C1,CHEMBL540634,1,5.700000503883016,0.1819195644711213,0
+O=C(c1ccc(F)cc1)N1CCN(c2ccc(OC3CCN(C4CCCC4)CC3)cc2)C(=O)C1,CHEMBL239963,1,5.599999383023619,0.1819195644711213,0
+O=C(c1ccc(F)cc1)N1CCN(c2ccc(OCCCN3CCCC3)cc2)C(=O)C1,CHEMBL429458,1,5.599999383023619,0.1819195644711213,0
+O=C(c1ccc(F)cc1F)N1CCN(c2ccc(OC3CCN(C4CCCC4)CC3)cc2)C(=O)C1,CHEMBL392354,1,5.599999383023619,0.1819195644711213,0
+O=C(c1ccc(OCCCN2CC[C@H](NS(=O)(=O)c3ccc(Br)cc3)C2)cc1)C1CC1,CHEMBL321709,1,5.823908740944319,0.1819195644711213,1
+O=C(c1ccc(OCCCN2CC[C@H](NS(=O)(=O)c3cccc(Cl)c3)C2)cc1)C1CC1,CHEMBL321320,1,5.443697499232711,0.1819195644711213,0
+O=C1NCN(c2ccccc2)C12CCN(CCCCc1nc3ccccc3s1)CC2,CHEMBL3819567,2,7.613765798473357,1.3161963349188852,1
+OC1(c2ccc(Cl)cc2)CC2CCC(C1)N2CCCCc1ccc(F)cc1,CHEMBL3321788,1,5.764724123312948,0.1819195644711213,0
+OC12CC3CC(CC(C3)N1Cc1cccc(F)c1)C2,CHEMBL1824778,1,5.0,0.1819195644711213,0
+OCCOCCN1CCN(C(c2ccccc2)c2ccc(Cl)cc2)CC1,CHEMBL896,1,8.698970004336019,0.1639776038099834,1
+c1ccc(OCCn2c(C3CCCNC3)nc3ccccc32)cc1,CHEMBL1935438,1,9.0,0.1140853274850961,1
+c1ccc2c(c1)CC(CCN1CCC1)=C2Cc1cnccn1,CHEMBL1669412,1,8.366531544420411,0.1639776038099834,1
+CC(c1nccs1)c1c(CCN(C)C)sc2ccccc12,CHEMBL1092767,1,7.455931955649724,0.4081691649627073,1
+CC1(C)CCCN(CCCOc2ccc(C3CCN(C(=O)c4ccc(CCC(=O)O)c5ccccc45)CC3)cc2)C1,CHEMBL2151156,1,7.899974269892138,0.4081691649627073,1
+CCN1CCN(CCCNC(=O)c2ccc(F)cc2Cl)CC1,CHEMBL4166021,1,5.0,0.1819195644711213,0
+CCOc1cc(CN2CCC(Nc3nc4cc(NS(=O)(=O)c5ccccc5)ccc4o3)CC2)ccc1OC,CHEMBL1077908,1,6.701146923590295,0.0956684631251178,1
+CN(C)CCc1sc2ccccc2c1Cc1ccc(F)cc1,CHEMBL540982,1,7.958607314841775,0.4081691649627073,1
+CN1CCC(/C(=N/O)c2nc3cc(Cl)ccc3[nH]2)CC1,CHEMBL1632414,1,6.0,0.0956684631251178,1
+CN1CCN(c2nc(N)nc3c2CCc2ccccc2-3)CC1,CHEMBL494093,1,5.039999923502187,0.1819195644711213,0
+COc1ccc(N2CCN(CCCNC(=O)Cn3c(=O)c4cccn4c4c(C)ccnc43)CC2)cc1,CHEMBL1729417,1,6.0,0.0956684631251178,1
+C[C@@H](c1cscn1)c1c(CCN(C)C)sc2ccccc12,CHEMBL1091782,1,8.337242168318426,0.1639776038099834,1
+C[C@H](C1=C(CCN(C)Cc2ccco2)Cc2ccccc21)c1cnccn1,CHEMBL1669418,1,8.09151498112135,0.1639776038099834,1
+Cc1cccc(N2CCN(CCCNC(=O)c3cn[nH]c3C)CC2)c1,CHEMBL3461530,1,7.356547323513813,0.4081691649627073,1
+Nc1nc2c(c(N3CCNCC3)n1)CCC1=C2[C@@H]2CCCC[C@@H]2O1,CHEMBL519240,1,5.450000476425332,0.1819195644711213,0
+O=C(O)c1cccnc1N1CCC(CN2CCC(Oc3ccc(Cl)c(Cl)c3)CC2)CC1,CHEMBL2158834,1,8.0,0.1639776038099834,1
+O=C(c1c[nH]c(=O)c2ccccc12)N1CCC(N2CCC(Oc3ccc(Cl)c(Cl)c3)CC2)CC1,CHEMBL2171020,1,7.599980364934843,0.4081691649627073,1
+O=c1c2ccccc2c(Cc2ccc(Cl)cc2)nn1C1CCCN(CCCCc2ccc(OCCCN3CCCCCC3)cc2)CC1,CHEMBL1767161,1,7.700057099977232,0.4081691649627073,1
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+OC1(c2ccc(Cl)cc2)CCN(CCCSc2ccc(F)cc2)CC1,CHEMBL1940403,1,6.939302159646387,0.0956684631251178,1
+c1ccc(CCCCNCC2c3ccccc3Cc3ccccc32)cc1,CHEMBL597528,1,6.015922966097169,0.0956684631251178,1
+c1ccc(CCCNCC2c3ccccc3Cc3ccccc32)cc1,CHEMBL609328,1,6.139661993429006,0.0956684631251178,1
+c1ccc(Cc2ccccc2OCCN2CCCCCC2)cc1,CHEMBL3943575,1,7.199970640755867,0.4081691649627073,1
+C=CCn1c(=N)n(CC(=O)c2ccccc2)c2ccccc21,CHEMBL1617548,1,4.970000123715015,0.4719824216245228,0
+CC(=O)c1ccc(OCCCN2CC[C@H](NC(=O)c3ccc(C#N)cc3)C2)cc1,CHEMBL430502,1,4.886056647693163,0.4719824216245228,0
+CC(=O)c1ccc(OCCCN2CC[C@H](NS(=O)(=O)c3ccc(C#N)cc3)C2)cc1,CHEMBL102840,1,4.853871964321762,0.4719824216245228,0
+CC(C)(C)c1ccc(SCC(c2ccccn2)c2c[nH]cn2)cc1,CHEMBL1243335,1,5.0,0.1819195644711213,0
+CCCCCC(=O)c1ccc(OCCCN2CCN(C(=O)c3cccnc3)CC2)cc1,CHEMBL418658,1,6.139997578098903,0.0956684631251178,1
+CCCc1ccc2c(c1)sc(=O)n2CCN1CCCCCC1,CHEMBL272899,1,6.507239610973163,0.0956684631251178,1
+CCOC(=O)N1CCN(CCCOc2ccc(-c3cc[nH]c3)cc2)CC1,CHEMBL101692,1,6.299997602857746,0.0956684631251178,1
+CN(C)CCCC1(c2ccc(F)cc2)OCc2cc(C#N)ccc21,CHEMBL549,1,6.430626090384954,0.0956684631251178,1
+CN(CCCCc1c[nH]cn1)CCC(c1ccc(Cl)cc1)c1ccccn1,CHEMBL292195,2,8.154901959985743,0.1639776038099834,1
+CN1CCC[C@@H]1Cn1nc(CC2CCCCC2)c2ccccc2c1=O,CHEMBL1767151,1,7.100015437450608,0.4081691649627073,1
+CN1CCc2c(c3cccc4c3n2CCc2ccccc2-4)C1,CHEMBL3104093,1,5.0,0.1819195644711213,0
+COc1ccc(CN(CCN[C@H]2CCN(C(=O)c3cc4ccccc4[nH]3)C2)c2ccccn2)cc1,CHEMBL1910384,1,6.3399982798971175,0.0956684631251178,1
+C[C@H](C1=C(CCN(C)Cc2ccncc2)Cc2ccccc21)c1cnccn1,CHEMBL1669425,1,8.309803919971486,0.1639776038099834,1
+Cc1noc2cc3c(cc12)CCN(CCCSc1nnc(-c2ccc(C(F)(F)F)cc2)n1C)CC3,CHEMBL401576,1,6.19999817218203,0.0956684631251178,1
+Fc1ccc(CCCCN2CCN(c3ccccn3)CC2)cc1,CHEMBL1940418,1,6.777283528852418,0.0956684631251178,1
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index 00000000..74268d91
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index 00000000..b5424cb7
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+72,O=C(O)c1ccc(Nc2nc(Cl)c(Cl)c(Nc3ccccc3C(=O)O)n2)cc1,CHEMBL2170423,5.826813731587727,,1
+73,CC(CO)NC(=N)c1c(O)nsc1Nc1ccc(Nc2ccccc2)cc1,CHEMBL253116,6.576754126063192,,1
+74,c1cc(N2CCOCC2)ccc1Nc1ncc2ccn(C3CCCCC3)c2n1,CHEMBL385744,7.309803919971486,,1
+75,Cn1cc(-c2cc3cnc(Nc4ccc(C(=O)N5CCS(=O)(=O)CC5)cc4Cl)cc3n2C(=O)OC(C)(C)C)cn1,CHEMBL3109939,4.275724130399212,,1
+76,O=C(O)/C=C\C(=O)Nc1cccc(-c2n[nH]c3ccc(NC(=O)Nc4ccccc4)cc23)c1,CHEMBL3901797,6.585026652029183,,1
+77,Cc1cccc(Cc2cc(Nc3nc(C)cn4c(-c5cn[nH]c5)cnc34)sn2)n1,CHEMBL4228308,8.397940008672037,>,1
+78,CNCCC(Nc1ncnc2c(C(N)=O)cccc12)c1cccc([N+](=O)[O-])c1,CHEMBL3675485,6.0,,1
+79,Cc1cc(Nc2cc(N3CCOCC3)nc(Nc3ccc([N+](=O)[O-])cc3)n2)n[nH]1,CHEMBL3237168,7.958607314841775,,1
+80,Cc1cn2c(-c3cnn(Cc4noc(-c5ccccc5)n4)c3)cnc2c(Nc2cc(CN3CCCCC3)ns2)n1,CHEMBL2023291,8.397940008672037,>,1
+81,Cn1cc(-c2ccc(Nc3cc4[nH]c(-c5cnn(C)c5)cc4cn3)c(Cl)c2)cn1,CHEMBL3109942,4.0,<,0
+82,Cc1cc(Nc2nn(Cc3ccc([N+](=O)[O-])cc3)c(=O)c3ccccc23)n[nH]1,CHEMBL1651483,6.962573502059376,,1
+83,O=C(Nc1ncc(CCNc2ncnc3ccsc23)s1)Nc1cccc2cc[nH]c12,CHEMBL486877,7.522878745280337,,1
+84,Cc1nn(C)cc1-c1nc2c(Oc3ccc(C(=O)Nc4ccncc4)cc3)c(Cl)cnc2[nH]1,CHEMBL3087819,6.478909202742116,,1
+85,Nc1ncnn2ccc(C(=O)Nc3ccc(NC(=O)Nc4ccsc4)cc3)c12,CHEMBL2063467,8.096910013008056,,1
+86,Cc1cn2c(-c3cn[nH]c3)cnc2c(Nc2cc(CN3C[C@@H]4CC[C@H](C3)O4)ns2)n1,CHEMBL1269988,8.397940008672037,>,1
+87,CO/N=C1C(=C2\C(=O)Nc3c(F)cccc32)\Nc2ccccc2\1,CHEMBL397720,4.0,<,0
+88,COc1ccc(NC(=O)CC(=O)Nc2ccc3c(c2)NC(=O)/C3=C\c2[nH]c(C)c(C(=O)O)c2C)cc1,CHEMBL3322565,6.692503962086787,,1
+89,COc1ccc(/C=N/Nc2[nH]nc(C)c2C(=O)OCc2ccccc2)cc1,CHEMBL517571,5.279840696594043,,1
+90,CC1=C(C(=O)Nc2ccc(Cl)cc2)C(c2ccccc2)C2=C(CC(C)(C)CC2=O)N1,CHEMBL1277474,4.394264106123253,,1
+91,NC(=O)c1cccc2c(NC(CCN3CCCCC3)c3cccc(NC(=O)c4c(F)cccc4F)c3)ncnc12,CHEMBL3680419,8.508638306165727,,1
+92,Cc1cn2c(-c3cnn(CC(=O)Nc4cccnc4)c3)cnc2c(Nc2cc(CN3CCCC(C)C3)ns2)n1,CHEMBL2022378,6.730487055782082,,1
+93,COc1cc(O)c2c(c1)CCC[C@H](O)CC(=O)/C=C\C[C@H](C)OC2=O,CHEMBL1651521,5.0,<,0
+94,Cc1csc(Nc2nccn3c(-c4cnn(C)c4)cnc23)c1,CHEMBL1801106,5.6020599913279625,,1
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+96,CNc1cc(C(=O)Nc2cccc(CNc3ncnc4c(C(N)=O)cccc34)c2)ccn1,CHEMBL3675532,9.82390874094432,,1
+97,CC(C)c1ccccc1Nc1ncc2c(n1)-c1c(c(C(N)=O)nn1C)CC2,CHEMBL1095772,5.0,<,0
+98,CC1=C(C(=O)Nc2ccc(F)c(Cl)c2)C(c2ccco2)n2c(nc3ccccc32)N1,CHEMBL1277383,5.24184537803261,,1
+99,CC(=O)Nc1ccc(Sc2nc(Nc3cc(C)[nH]n3)cc(N3CCN(C)CC3)n2)cc1,CHEMBL4228587,9.0,,1
+100,O=C(Nc1ccc(F)cc1)c1ccc(Nc2nc(Nc3ccc(O)cc3)ncc2F)cc1,CHEMBL522791,7.602059991327962,,1
+101,Cc1cc(Nc2nc(C(F)(F)F)cn3c(-c4cn[nH]c4)cnc23)sn1,CHEMBL1801112,7.568636235841013,,1
+102,Cc1[nH]nc2c1Cc1c-2[nH]c2ccccc12,CHEMBL469204,6.060480747381384,,1
+103,COc1cc2nc(Nc3cc(C)[nH]n3)nc(Nc3cccc(-c4nc5ccccc5s4)c3)c2cc1OC,CHEMBL522785,5.259637310505756,,1
+104,CN1CCN(c2ccc(C(=O)Nc3cc(-c4ccc(NC(=O)OCc5ccccc5)cc4)n[nH]3)cc2)CC1,CHEMBL2063319,6.0,<,0
+105,Cc1cc(Nc2nc(SC(C)(C)C)cn3c(-c4cn[nH]c4)cnc23)sn1,CHEMBL1801133,6.978810700930063,,1
+106,CCn1cc(-c2ccnc3[nH]c(-c4cccc(CN5CCCC5)c4)cc23)c(-c2ccc(NC(=O)Nc3ccccc3)cc2)n1,CHEMBL1099349,7.657577319177793,,1
+107,CNCC(Nc1ncnc2c(C(N)=O)cccc12)c1cccc(NC(=O)c2ccc(OC)cc2F)c1,CHEMBL3675427,9.468521082957745,,1
+108,NC(=O)c1cccc2c(NCc3ccc(F)c(NC(=O)c4ccc(OC(F)(F)F)cc4)c3)ncnc12,CHEMBL3685287,9.657577319177792,,1
+109,COc1cnc(C(=O)Nc2cccc([C@@H](C)Nc3ncnc4c(C(N)=O)cccc34)c2)cn1,CHEMBL3680563,9.292429823902063,,1
+110,O=C(Nc1cccc(C(F)(F)F)c1)Nc1nc(CCNc2ncnc3ccsc23)cs1,CHEMBL513076,4.698970004336019,<,0
+111,COc1ccc(C(=O)Nc2ccc(C)c(CNc3ncnc4c(C(N)=O)cccc34)c2)cc1,CHEMBL3685211,8.040958607678906,,1
+112,COc1cc2ncnc(Nc3ccc(-c4nc5ccccc5s4)c(O)c3)c2cc1OCCCN1CCN(C)CC1,CHEMBL604485,6.522878745280337,,1
+113,CN(O)C(=O)c1nn(C)c2c1CCc1cnc(NC3CCCC3)nc1-2,CHEMBL599824,5.514704561273911,,1
+114,NC(=O)c1cccc2c(NCc3ccc(Cl)c(C(F)(F)F)c3)ncnc12,CHEMBL3675365,6.130768280269022,,1
+115,NC(=O)c1cccc2c(NCc3cccc(NC(=O)c4ccc(F)c(F)c4)c3)ncnc12,CHEMBL3670473,8.958607314841776,,1
+116,COc1cc2ncnc(Nc3ccc(-c4nc5ccccc5s4)cc3F)c2cc1OCCCN1CCN(C)CC1,CHEMBL594661,6.4089353929735005,,1
+117,COc1cc2c(Nc3cnc(NC(=O)c4ccc(C)c(Br)c4)nc3)ncnc2cc1OCCCN1CCOCC1,CHEMBL203007,7.154901959985742,,1
+118,Cn1cc(-c2cc3cnc(Nc4ccc(-c5cnccn5)cc4Cl)cc3n2C(=O)OC(C)(C)C)cn1,CHEMBL3109947,4.0,<,0
+119,OCCOCCNc1ncnc2oc(-c3ccccc3)c(-c3ccccc3)c12,CHEMBL1172831,6.1191864077192095,,1
+120,Cn1nc(C(N)=O)c2c1-c1nc(Nc3ccccc3Cc3ccccc3)ncc1CC2,CHEMBL1094488,5.0,<,0
+121,CCOC(=O)c1[nH]cc2c1NC1=C(C(=O)CCC1)C2c1ccc(Sc2nc3cccc(Cl)c3[nH]2)o1,CHEMBL3393479,8.397940008672037,,1
+122,C[C@@H](O)CNc1ncnc2oc(-c3ccccc3)c(-c3ccccc3)c12,CHEMBL1172716,6.011887159731648,,1
+123,CN1CCC(Oc2ccc(-c3n[nH]c4ccc(C(=O)NC(c5ccccn5)C5CCCC5)cc34)cc2)CC1,CHEMBL3422071,5.0,<,0
+124,Cc1cn2c(-c3cnn(Cc4nc(-c5ccccc5)c[nH]4)c3)cnc2c(Nc2cc(CN3CCCCC3)ns2)n1,CHEMBL2023292,6.234331445240986,,1
+125,COc1cc(C(N)=O)c2ncnc(NC(CN(C)C)c3ccccc3OC)c2c1,CHEMBL3680524,6.327902142064282,,1
+126,CNC(=O)c1nn(C)c2c1C(C)(C)Cc1cnc(Nc3cccc(OC4CCN(C)CC4)c3)nc1-2,CHEMBL560102,5.800244822746524,,1
+127,Cc1cc(C(N)=O)c2ncnc(NCc3cccc(F)c3)c2c1,CHEMBL3670456,5.886056647693162,,1
+128,CCNCc1cncc(/C=C/c2c[nH]nc2-c2nc3ccc(OC)cc3[nH]2)c1,CHEMBL245967,6.229147988357855,,1
+129,COc1ccc(C(=O)Nc2cccc(C(C)Nc3ncnc4c(C(N)=O)cc(C#N)cc34)c2)cc1F,CHEMBL3685310,9.585026652029182,,1
+130,NC(=O)c1cccc2c(NC(CCN3CCC3)c3cccc(NC(=O)c4ccc(Br)cc4)c3)ncnc12,CHEMBL3675518,9.236572006437065,,1
+131,Cc1ccnc(Cc2cc(Nc3nc(C)cn4c(-c5cn[nH]c5)cnc34)sn2)c1,CHEMBL4226657,8.397940008672037,>,1
+132,Cc1cc(Nc2cc(N3CCN(C)CC3)nc(Nc3ccc(N4CCOCC4)cc3)n2)n[nH]1,CHEMBL3237190,6.950781977329817,,1
+133,CC(=O)Nc1ccc(Cn2nc(Nc3cc[nH]n3)c3ccccc3c2=O)cc1,CHEMBL1651491,6.259637310505756,,1
+134,NC(=O)c1cccc2c(NCc3cccc(NC(=O)c4ccc(CO)cc4)c3)ncnc12,CHEMBL3680571,9.080921907623924,,1
+135,OCCNc1ncnc2oc(-c3ccccc3)c(-c3ccccc3)c12,CHEMBL189434,6.536939436767204,,1
+136,O=C(CCc1cn(Cc2ccc(Cl)cc2)c2ccccc12)Nc1ccncc1,CHEMBL185140,4.0,<,0
+137,Cc1cc2[nH]nc(/C=C/c3cccc(NC(=O)N4CCN(C)CC4)c3)c2cc1NC(=O)Cc1cccs1,CHEMBL1093265,7.886056647693162,,1
+138,Cc1ccc(C(=O)Nc2cccc([C@@H](C)Nc3ncnc4c(C(N)=O)cccc34)c2)cc1C,CHEMBL3675455,9.568636235841012,,1
+139,CC(Nc1ncnc2c(C(N)=O)cccc12)c1cccc(NC(=O)C2=CCC(Br)C=C2)c1,CHEMBL3675466,9.585026652029182,,1
+140,CN(C)C(=O)[C@]1(Cc2cccc(Nc3cc[nH]n3)n2)CC[C@@H](Oc2cccc(Cl)c2F)CC1,CHEMBL3986649,8.886056647693161,,1
+141,Cc1cc(CN2CCN(c3c(Br)cnc4[nH]c(-c5ccc(CN(C)C)cc5)nc34)CC2)no1,CHEMBL1085833,7.853871964321763,,1
+142,Cn1cc(C2=C(c3nn(CCn4ccnc4)c4ncccc34)C(=O)NC2=O)c2ccccc21,CHEMBL563560,4.3979400086720375,<,0
+143,O=C(Nc1n[nH]c2cc(C(=O)NCc3ccccc3)sc12)c1ccc(N2CCOCC2)cc1,CHEMBL1258794,7.958607314841775,,1
+144,O=C(Nc1ccc(CCNc2ncnc3oc(-c4ccccc4)c(Cl)c23)cc1)Nc1cccc(CN2CCCCC2)c1,CHEMBL4171792,7.044793462458057,,1
+145,O=C(Nc1ccc(Nc2ccnc3[nH]c(=O)c4ccccc4c23)cc1)c1ccccc1C(F)(F)F,CHEMBL2380824,8.397940008672037,,1
+146,O=C(O)c1cccc(Nc2ncc(F)c(Nc3ccccc3Cl)n2)c1,CHEMBL2170589,8.346787486224656,,1
+147,Cc1nc(Nc2cc(-c3ccc(CNC(=O)OCc4cccnc4)cc3)n[nH]2)cc(N2CCN(CCO)CC2)n1,CHEMBL2063434,6.0,<,0
+148,CC(C)N(C)C[C@@H](Nc1ncnc2c(C(N)=O)cccc12)c1ccccc1,CHEMBL3675526,6.853871964321763,,1
+149,O=C1OC(Nc2ncc3nnn(-c4ccccc4)c3n2)c2ccccc21,CHEMBL253956,5.136677139879544,,1
+150,CCNC(=O)c1cc(NC(C)(C)C(=O)Nc2nc3c(C(F)(F)F)cc4[nH]ncc4c3s2)ccc1Cl,CHEMBL3719096,6.787812395596043,,1
+151,CC(C)NC[C@@H](Nc1ncnc2c(C(N)=O)cccc12)c1ccccc1,CHEMBL3680414,6.096910013008058,,1
+152,O=C(Nc1ccc(CCNc2ncnc3ccsc23)cc1)c1cccc(C(F)(F)F)c1,CHEMBL459888,5.494850021680094,,1
+153,Cc1cn2c(-c3cn[nH]c3)cnc2c(Nc2cc(CN3CC[C@@H](F)C3)ns2)n1,CHEMBL1650544,8.397940008672037,>,1
+154,COc1ccc(Nc2nc(Nc3cc(C)[nH]n3)cc(N3CCN(C)CC3)n2)c(OC)c1OC,CHEMBL4164598,7.026872146400303,,1
+155,COc1cc2ncnc(Nc3ccc(-c4nc5ccccc5s4)cc3O)c2cc1OC,CHEMBL593293,5.0,<,0
+156,O=C(O)c1ccc(Nc2nccc(Nc3ccccc3)n2)cc1,CHEMBL2170597,8.012652498338772,,1
+157,COc1ccc(NC(=O)Nc2ccc(CCNc3ncnc4oc(-c5ccccc5)c(-c5ccccc5)c34)cc2)cc1,CHEMBL2398653,6.435333935747911,,1
+158,CC(C)S(=O)(=O)Nc1cccc(-c2cc(NC(=O)c3ccc(N4CCN(C)CC4)cc3)[nH]n2)c1,CHEMBL1807474,6.0,<,0
+159,CC(=O)Nc1cccc(-c2ccc(C(N)=O)c3[nH]c4cc(C(=O)N5CCN(C)CC5)ccc4c23)c1C,CHEMBL3621515,5.698970004336019,<,0
+160,Cc1cn2c(-c3cnn(CC(=O)N(C)Cc4ccccc4)c3)cnc2c(Nc2cc(CN3CCCC(C)C3)ns2)n1,CHEMBL2022122,5.788612447063142,,1
+161,NC(=O)CCCCc1cccc(Nc2nccc(Nc3cccc(C(F)(F)F)c3)n2)c1,CHEMBL3917769,5.089668776347804,,1
+162,CNC(=O)Nc1sc2c(C(C)C)cc(NS(=O)(=O)N(C)C)cc2c1C(N)=O,CHEMBL4290876,8.045757490560675,,1
+163,COc1cc2c(Oc3ccc(-c4cnc(Cc5ccccc5)n(C)c4=O)cc3F)ccnc2cc1OCCCN1CCOCC1,CHEMBL505896,6.935542010773082,,1
+164,O=C(O)c1cccc(Nc2ncc3ccn(C4CCCCC4)c3n2)c1,CHEMBL1643136,9.221848749616356,,1
+165,Nc1nc(-c2ccccc2)cc(-c2ccc3c(N)n[nH]c3c2)n1,CHEMBL1765758,5.800000874803202,,1
+166,COc1ccccc1Nc1nc(Nc2ccc(O)cc2)ncc1F,CHEMBL522476,7.698970004336019,,1
+167,[N-]=[N+]=NC[C@@H](Nc1ncnc2c(C(N)=O)cccc12)c1ccccc1,CHEMBL3670458,6.346787486224656,,1
+168,Cc1nc(Nc2cc(-c3ccc(CNC(=O)Nc4cc(C(C)(C)C)on4)cc3)n[nH]2)cc(N2CCNCC2)n1,CHEMBL2386809,6.1487416512809245,,1
+169,COc1cc2c(Nc3ncc(CC(=O)Nc4cccc(F)c4)s3)ncnc2cc1OCCN1CCC(CCO)CC1,CHEMBL202593,9.0,>,1
+170,O=C(Nc1cccc(C(F)(F)F)c1)Nc1cn(CCNc2ncnc3ccsc23)cn1,CHEMBL462354,5.920818753952375,,1
+171,COc1ccc(CCNC(=O)c2c(N)ncnc2Nc2ccc(OCc3ccccc3)c(Cl)c2)cc1,CHEMBL461115,4.0,<,0
+172,NC(=O)c1cccc2c(NCc3cccc(NC(=O)c4c(F)cc(Cl)cc4F)c3)ncnc12,CHEMBL3685305,10.096910013008056,,1
+173,NC(=O)c1cccc2c(NCc3cccc(NS(=O)(=O)c4ccccc4)c3)ncnc12,CHEMBL3675370,5.309803919971486,,1
+174,Cn1nc(C(N)=O)c2c1-c1nc(NC3CCN(C(=O)C4CCN(S(C)(=O)=O)CC4)CC3)ncc1CC2,CHEMBL597753,5.907630300370879,,1
+175,Cc1ccc(C(CN)Nc2ncnc3c(C(N)=O)cccc23)cc1,CHEMBL3670404,6.180456064458131,,1
+176,COc1ccc(C(=O)Nc2cccc(C(C)Nc3ncnc4c(C(N)=O)cc(C)cc34)c2)cc1F,CHEMBL3685300,8.619788758288394,,1
+177,O=C(NCc1ccc(-c2cc(NC(=O)c3ccc(OCCN4CCOCC4)cc3)[nH]n2)cc1)Nc1ccc(Cl)cc1,CHEMBL2386800,6.0,<,0
+178,CCc1cccc(CC)c1NC(=O)c1nn(C)c2c1CCc1cnc(Nc3ccc(C(=O)N4CCC[C@@H]4CN4CCCC4)cc3OC(F)(F)F)nc1-2,CHEMBL1807300,5.0,<,0
+179,O=C1CNCC2=C1C(c1cccc(Oc3ccc(Cl)cc3)c1)c1c[nH]nc1N2,CHEMBL3393458,5.562407967746038,,1
+180,COc1cc(C(N)=O)c2ncnc(NCc3cccc(C(F)(F)F)c3)c2c1,CHEMBL3670460,6.698970004336019,,1
+181,CCc1cccc(CC)c1NC(=O)c1nn(C)c2c1CCc1cnc(Nc3ccc(C(=O)NCCN(C)C)cc3OC(F)(F)F)nc1-2,CHEMBL1808342,5.861381566100508,,1
+182,NC(=O)c1cccc2c(NCc3cccc(NC(=O)c4ccc(Cl)c(Cl)c4)c3)ncnc12,CHEMBL3680403,9.537602002101044,,1
+183,CC(=O)c1ccc(Nc2ncc3c(n2)-c2c(c(C(N)=O)nn2C)CC3)cc1,CHEMBL1099106,6.935542010773082,,1
+184,O=C(O)/C=C\C(=O)Nc1cccc(-c2n[nH]c3ccc(NS(=O)(=O)c4ccccc4)cc23)c1,CHEMBL3986609,7.585026652029183,,1
+185,Cc1cc(CN2CCN(c3c(Cl)cnc4[nH]c(N5CCN(C)CC5)nc34)CC2)no1,CHEMBL1086582,8.0,,1
+186,CN(C)CCN(c1ccc(Nc2ncc3cnn(C4CCCCCC4)c3n2)cn1)S(C)(=O)=O,CHEMBL1825099,6.585026652029183,,1
+187,Cc1noc(C2(Cc3ccc(F)c(Nc4cc(C)[nH]n4)n3)CCN(C(=O)c3cccc(Cl)c3F)CC2)n1,CHEMBL3692217,9.096910013008056,,1
+188,Cc1cc(Nc2nc(-c3ccccc3)nc3cc(N4CCN(C)CC4)ccc23)n[nH]1,CHEMBL4292755,8.0,,1
+189,CN(CCO)c1ccc(C(=O)Nc2cccc(CNc3ncnc4c(C(N)=O)cccc34)c2)cn1,CHEMBL3680387,9.161150909262744,,1
+190,O=C(Nc1ccccc1)Nc1ccc(-c2nn(CC3CCCO3)cc2-c2ccnc3[nH]ccc23)cc1,CHEMBL1964276,5.207958689287918,,1
+191,Cc1cnc(C(=O)Nc2cccc([C@@H](C)Nc3ncnc4c(C(N)=O)cccc34)c2)cn1,CHEMBL3680438,8.508638306165727,,1
+192,CNC(=O)c1nn(C)c2c1CCc1cnc(Nc3ccc(N4CCN(C)CC4)cc3)nc1-2,CHEMBL559844,6.7569619513137065,,1
+193,Cc1ccc(C(=O)Nc2cccc(C(C)Nc3ncnc4c(C(N)=O)cc(Cl)cc34)c2)cn1,CHEMBL3680573,9.065501548756432,,1
+194,CCOC(=O)c1cc(NC(C)(C)C(=O)Nc2nc3ccc4[nH]ncc4c3s2)ccc1Cl,CHEMBL3715109,6.97061622231479,,1
+195,CCN(CCO)CCCOc1cc2ncnc(Nc3ncc(CC(=O)Nc4cccc(F)c4)s3)c2cc1OC,CHEMBL202198,9.0,>,1
+196,COCCNC(=O)c1cccc2cc(Oc3ccnc4cc(OC)c(OC)cc34)ccc12,CHEMBL271188,5.142667503568732,,1
+197,C[C@@H](NC(=O)c1cc2[nH]nc(NC(=O)c3ccc(N4CCOCC4)cc3)c2s1)c1ccccc1,CHEMBL1258912,8.301029995663981,,1
+198,COc1ccc(-c2nc3c(N4CCN(CC(=O)Nc5nccs5)CC4)c(Cl)cnc3[nH]2)cc1,CHEMBL1082831,7.2839966563652,,1
+199,COc1ccc(C2=Nc3c(n[nH]c3C)Nc3nc(SC)ncc32)cc1,CHEMBL4291146,7.031517051446066,,1
diff --git a/atomsci/ddm/test/integrative/import_transformer/data/delaney-processed_curated_fit_short.csv b/atomsci/ddm/test/integrative/import_transformer/data/delaney-processed_curated_fit_short.csv
new file mode 100644
index 00000000..73695496
--- /dev/null
+++ b/atomsci/ddm/test/integrative/import_transformer/data/delaney-processed_curated_fit_short.csv
@@ -0,0 +1,201 @@
+Unnamed: 0,Compound ID,ESOL predicted log solubility in mols per litre,Minimum Degree,Molecular Weight,Number of H-Bond Donors,Number of Rings,Number of Rotatable Bonds,Polar Surface Area,measured log solubility in mols per litre,smiles,rdkit_smiles,inchi_key,compound_id,VALUE_NUM_mean,VALUE_NUM_std,Perc_Var,Remove_BadDuplicate,measured log solubility in mols per litre_class,measured log solubility in mols per litre2,measured log solubility in mols per litre_class2
+117,Anilofos,-5.106,1,367.86,0,1,7,38.77,-4.432,COP(=S)(OC)SCC(=O)N(C(C)C)c1ccc(Cl)cc1,COP(=S)(OC)SCC(=O)N(c1ccc(Cl)cc1)C(C)C,NXQDBZGWYSEGFL-UHFFFAOYSA-N,NXQDBZGWYSEGFL-UHFFFAOYSA-N,-4.432,,-0.0,0,0.0,-4.432,0.0
+118,Hexylbenzene ,-4.22,1,162.276,0,1,5,0.0,-5.21,CCCCCCc1ccccc1,CCCCCCc1ccccc1,LTEQMZWBSYACLV-UHFFFAOYSA-N,LTEQMZWBSYACLV-UHFFFAOYSA-N,-5.21,,-0.0,0,0.0,-5.21,0.0
+119,2-Chlorobiphenyl,-4.5280000000000005,1,188.657,0,2,1,0.0,-4.54,Clc1ccccc1c2ccccc2,Clc1ccccc1-c1ccccc1,LAXBNTIAOJWAOP-UHFFFAOYSA-N,LAXBNTIAOJWAOP-UHFFFAOYSA-N,-4.54,,-0.0,0,0.0,-4.54,0.0
+120,2-Methyl-1-Pentene,-2.3480000000000003,1,84.16199999999999,0,0,2,0.0,-3.03,CCCC(=C)C,C=C(C)CCC,WWUVJRULCWHUSA-UHFFFAOYSA-N,WWUVJRULCWHUSA-UHFFFAOYSA-N,-3.03,,-0.0,0,1.0,-3.03,1.0
+121,"2,3,4-Trimethylpentane",-3.276,1,114.232,0,0,2,0.0,-4.8,CC(C)C(C)C(C)C,CC(C)C(C)C(C)C,RLPGDEORIPLBNF-UHFFFAOYSA-N,RLPGDEORIPLBNF-UHFFFAOYSA-N,-4.8,,-0.0,0,0.0,-4.8,0.0
+122,Pentachlorobenzene,-5.167999999999999,1,250.339,0,1,0,0.0,-5.65,Clc1cc(Cl)c(Cl)c(Cl)c1Cl,Clc1cc(Cl)c(Cl)c(Cl)c1Cl,CEOCDNVZRAIOQZ-UHFFFAOYSA-N,CEOCDNVZRAIOQZ-UHFFFAOYSA-N,-5.65,,-0.0,0,0.0,-5.65,0.0
+123,m-Nitrophenol,-2.318,1,139.11,1,1,1,63.37,-1.01,Oc1cccc(c1)N(=O)=O,O=[N+]([O-])c1cccc(O)c1,RTZZCYNQPHTPPL-UHFFFAOYSA-N,RTZZCYNQPHTPPL-UHFFFAOYSA-N,-1.01,,-0.0,0,1.0,-1.01,1.0
+124,1-Decene,-3.781,1,140.26999999999998,0,0,7,0.0,-5.51,CCCCCCCCC=C,C=CCCCCCCCC,AFFLGGQVNFXPEV-UHFFFAOYSA-N,AFFLGGQVNFXPEV-UHFFFAOYSA-N,-5.51,,-0.0,0,0.0,-5.51,0.0
+125,Glyceryl triacetate,-1.285,1,218.205,0,0,5,78.9,-0.6,CC(=O)OCC(COC(=O)C)OC(=O)C,CC(=O)OCC(COC(C)=O)OC(C)=O,URAYPUMNDPQOKB-UHFFFAOYSA-N,URAYPUMNDPQOKB-UHFFFAOYSA-N,-0.6,,-0.0,0,1.0,-0.6,1.0
+126,dimethirimol,-3.57,1,209.293,1,1,4,49.25000000000001,-2.24,CCCCc1c(C)nc(nc1O)N(C)C ,CCCCc1c(C)nc(N(C)C)nc1O,CJHXCRMKMMBYJQ-UHFFFAOYSA-N,CJHXCRMKMMBYJQ-UHFFFAOYSA-N,-2.24,,-0.0,0,1.0,-2.24,1.0
+127,Cyfluthrin,-6.84,1,434.29400000000015,0,3,6,59.32000000000001,-7.337000000000001,CC1(C)C(C=C(Cl)Cl)C1C(=O)OC(C#N)c2ccc(F)c(Oc3ccccc3)c2,CC1(C)C(C=C(Cl)Cl)C1C(=O)OC(C#N)c1ccc(F)c(Oc2ccccc2)c1,QQODLKZGRKWIFG-UHFFFAOYSA-N,QQODLKZGRKWIFG-UHFFFAOYSA-N,-7.337000000000001,,-0.0,0,0.0,-7.337000000000001,0.0
+128,Pyridine,-1.481,2,79.10199999999998,0,1,0,12.89,0.76,c1ccncc1,c1ccncc1,JUJWROOIHBZHMG-UHFFFAOYSA-N,JUJWROOIHBZHMG-UHFFFAOYSA-N,0.76,,0.0,0,1.0,0.76,1.0
+129,1-Bromoheptane,-3.366,1,179.101,0,0,5,0.0,-4.43,CCCCCCCBr,CCCCCCCBr,LSXKDWGTSHCFPP-UHFFFAOYSA-N,LSXKDWGTSHCFPP-UHFFFAOYSA-N,-4.43,,-0.0,0,0.0,-4.43,0.0
+130,"3,4-Dimethylpyridine",-2.067,1,107.156,0,1,0,12.89,0.36,Cc1ccncc1C,Cc1ccncc1C,NURQLCJSMXZBPC-UHFFFAOYSA-N,NURQLCJSMXZBPC-UHFFFAOYSA-N,0.36,,0.0,0,1.0,0.36,1.0
+131,Fludrocortisone,-3.172,1,380.4560000000001,3,4,2,94.83,-3.43,CC34CC(O)C1(F)C(CCC2=CC(=O)CCC12C)C3CCC4(O)C(=O)CO ,CC12CCC(=O)C=C1CCC1C3CCC(O)(C(=O)CO)C3(C)CC(O)C12F,AAXVEMMRQDVLJB-UHFFFAOYSA-N,AAXVEMMRQDVLJB-UHFFFAOYSA-N,-3.43,,-0.0,0,0.0,-3.43,0.0
+132,ethiofencarb,-2.855,1,225.313,1,1,4,38.33,-2.09,CCSCc1ccccc1OC(=O)NC ,CCSCc1ccccc1OC(=O)NC,HEZNVIYQEUHLNI-UHFFFAOYSA-N,HEZNVIYQEUHLNI-UHFFFAOYSA-N,-2.09,,-0.0,0,1.0,-2.09,1.0
+133,Malonic acid diethylester,-1.413,1,160.16899999999998,0,0,4,52.60000000000001,-0.82,CCOC(=O)CC(=O)OCC,CCOC(=O)CC(=O)OCC,IYXGSMUGOJNHAZ-UHFFFAOYSA-N,IYXGSMUGOJNHAZ-UHFFFAOYSA-N,-0.82,,-0.0,0,1.0,-0.82,1.0
+134,d-Limonene,-3.429,1,136.238,0,1,1,0.0,-4.26,CC1=CCC(CC1)C(C)=C,C=C(C)C1CC=C(C)CC1,XMGQYMWWDOXHJM-UHFFFAOYSA-N,XMGQYMWWDOXHJM-UHFFFAOYSA-N,-4.26,,-0.0,0,0.0,-4.26,0.0
+135,Indan,-3.057,2,118.17899999999996,0,2,0,0.0,-3.04,C1Cc2ccccc2C1,c1ccc2c(c1)CCC2,PQNFLJBBNBOBRQ-UHFFFAOYSA-N,PQNFLJBBNBOBRQ-UHFFFAOYSA-N,-3.04,,-0.0,0,1.0,-3.04,1.0
+136,p-t-Butylphenol,-3.192,1,150.22099999999998,1,1,0,20.23,-2.41,CC(C)(C)c1ccc(O)cc1,CC(C)(C)c1ccc(O)cc1,QHPQWRBYOIRBIT-UHFFFAOYSA-N,QHPQWRBYOIRBIT-UHFFFAOYSA-N,-2.41,,-0.0,0,1.0,-2.41,1.0
+137,Cyclopropyl-5-spirobarbituric acid,-0.088,1,154.125,2,2,0,75.27,-1.886,O=C2NC(=O)C1(CC1)C(=O)N2 ,O=C1NC(=O)C2(CC2)C(=O)N1,PQVZDTWRGIHYCU-UHFFFAOYSA-N,PQVZDTWRGIHYCU-UHFFFAOYSA-N,-1.886,,-0.0,0,1.0,-1.886,1.0
+138,m-Chloroiodobenzene,-4.384,1,238.455,0,1,0,0.0,-3.55,Clc1cccc(I)c1,Clc1cccc(I)c1,JMLWXCJXOYDXRN-UHFFFAOYSA-N,JMLWXCJXOYDXRN-UHFFFAOYSA-N,-3.55,,-0.0,0,0.0,-3.55,0.0
+139,1-Bromonapthalene,-4.434,1,207.07,0,2,0,0.0,-4.35,Brc1cccc2ccccc12,Brc1cccc2ccccc12,DLKQHBOKULLWDQ-UHFFFAOYSA-N,DLKQHBOKULLWDQ-UHFFFAOYSA-N,-4.35,,-0.0,0,0.0,-4.35,0.0
+140,trans-2-Pentene ,-2.076,1,70.135,0,0,1,0.0,-2.54,CC/C=C/C,C/C=C/CC,QMMOXUPEWRXHJS-HWKANZROSA-N,QMMOXUPEWRXHJS-HWKANZROSA-N,-2.54,,-0.0,0,1.0,-2.54,1.0
+141,"2,6-Dimethylpyridine",-2.0980000000000003,1,107.156,0,1,0,12.89,0.45,Cc1cccc(C)n1,Cc1cccc(C)n1,OISVCGZHLKNMSJ-UHFFFAOYSA-N,OISVCGZHLKNMSJ-UHFFFAOYSA-N,0.45,,0.0,0,1.0,0.45,1.0
+142,Trichloroethylene,-2.312,1,131.389,0,0,0,0.0,-1.96,ClC=C(Cl)Cl,ClC=C(Cl)Cl,XSTXAVWGXDQKEL-UHFFFAOYSA-N,XSTXAVWGXDQKEL-UHFFFAOYSA-N,-1.96,,-0.0,0,1.0,-1.96,1.0
+143,1-Napthylamine,-2.721,1,143.189,1,2,0,26.02,-1.92,Nc1cccc2ccccc12,Nc1cccc2ccccc12,RUFPHBVGCFYCNW-UHFFFAOYSA-N,RUFPHBVGCFYCNW-UHFFFAOYSA-N,-1.92,,-0.0,0,1.0,-1.92,1.0
+144,m-Xylene ,-3.035,1,106.168,0,1,0,0.0,-2.82,Cc1cccc(C)c1,Cc1cccc(C)c1,IVSZLXZYQVIEFR-UHFFFAOYSA-N,IVSZLXZYQVIEFR-UHFFFAOYSA-N,-2.82,,-0.0,0,1.0,-2.82,1.0
+145,2-hydroxypteridine,-1.404,1,148.125,1,2,0,71.79,-1.947,Oc2ncc1nccnc1n2,Oc1ncc2nccnc2n1,VWXIHLCLIOQWRA-UHFFFAOYSA-N,VWXIHLCLIOQWRA-UHFFFAOYSA-N,-1.947,,-0.0,0,1.0,-1.947,1.0
+146,Methanol,0.441,1,32.042,1,0,0,20.23,1.57,CO,CO,OKKJLVBELUTLKV-UHFFFAOYSA-N,OKKJLVBELUTLKV-UHFFFAOYSA-N,1.57,,0.0,0,1.0,1.57,1.0
+147,Amobarbital,-2.312,1,226.276,2,1,4,75.27000000000001,-2.468,CCC1(CCC(C)C)C(=O)NC(=O)NC1=O,CCC1(CCC(C)C)C(=O)NC(=O)NC1=O,VIROVYVQCGLCII-UHFFFAOYSA-N,VIROVYVQCGLCII-UHFFFAOYSA-N,-2.563,0.1343502884254441,-3.706593835349191,0,1.0,-2.468,1.0
+148,2-Butanone,-0.491,1,72.107,0,0,1,17.07,0.52,CCC(=O)C,CCC(C)=O,ZWEHNKRNPOVVGH-UHFFFAOYSA-N,ZWEHNKRNPOVVGH-UHFFFAOYSA-N,0.52,,0.0,0,1.0,0.52,1.0
+149,5-fluorouracil,-0.792,1,130.078,2,1,0,65.72,-1.077,Fc1c[nH]c(=O)[nH]c1=O ,O=c1[nH]cc(F)c(=O)[nH]1,GHASVSINZRGABV-UHFFFAOYSA-N,GHASVSINZRGABV-UHFFFAOYSA-N,-1.077,,-0.0,0,1.0,-1.077,1.0
+150,tubercidin,-0.892,1,266.257,4,3,2,126.65,-1.95,Nc1ncnc2n(ccc12)C3OC(CO)C(O)C3O ,Nc1ncnc2c1ccn2C1OC(CO)C(O)C1O,HDZZVAMISRMYHH-UHFFFAOYSA-N,HDZZVAMISRMYHH-UHFFFAOYSA-N,-1.95,,-0.0,0,1.0,-1.95,1.0
+151,"1,3-Benzenediol",-1.59,1,110.11199999999998,2,1,0,40.46,0.81,Oc1cccc(O)c1,Oc1cccc(O)c1,GHMLBKRAJCXXBS-UHFFFAOYSA-N,GHMLBKRAJCXXBS-UHFFFAOYSA-N,0.81,,0.0,0,1.0,0.81,1.0
+152,1-Hexanol,-1.3969999999999998,1,102.177,1,0,4,20.23,-1.24,CCCCCCO,CCCCCCO,ZSIAUFGUXNUGDI-UHFFFAOYSA-N,ZSIAUFGUXNUGDI-UHFFFAOYSA-N,-1.24,,-0.0,0,1.0,-1.24,1.0
+153,1-Chloropentane,-2.294,1,106.596,0,0,3,0.0,-2.73,CCCCCCl,CCCCCCl,SQCZQTSHSZLZIQ-UHFFFAOYSA-N,SQCZQTSHSZLZIQ-UHFFFAOYSA-N,-2.73,,-0.0,0,1.0,-2.73,1.0
+154,"1,3-Butadiene",-1.376,1,54.09199999999999,0,0,1,0.0,-1.87,C=CC=C,C=CC=C,KAKZBPTYRLMSJV-UHFFFAOYSA-N,KAKZBPTYRLMSJV-UHFFFAOYSA-N,-1.87,,-0.0,0,1.0,-1.87,1.0
+155,Propyl acetate,-1.125,1,102.13299999999998,0,0,2,26.3,-0.72,CCCOC(=O)C,CCCOC(C)=O,YKYONYBAUNKHLG-UHFFFAOYSA-N,YKYONYBAUNKHLG-UHFFFAOYSA-N,-0.72,,-0.0,0,1.0,-0.72,1.0
+156,"5,6,7,8-tetrahydro-2-naphthol",-3.0860000000000003,1,148.205,1,2,0,20.23,-1.99,Oc2ccc1CCCCc1c2 ,Oc1ccc2c(c1)CCCC2,UMKXSOXZAXIOPJ-UHFFFAOYSA-N,UMKXSOXZAXIOPJ-UHFFFAOYSA-N,-1.99,,-0.0,0,1.0,-1.99,1.0
+157,chloroacetamide,-0.106,1,93.513,1,0,1,43.09,-0.02,NC(=O)CCl ,NC(=O)CCl,VXIVSQZSERGHQP-UHFFFAOYSA-N,VXIVSQZSERGHQP-UHFFFAOYSA-N,-0.02,,-0.0,0,1.0,-0.02,1.0
+158,Iodofenphos,-6.148,1,413.0,0,1,4,27.69,-6.62,COP(=S)(OC)Oc1cc(Cl)c(I)cc1Cl,COP(=S)(OC)Oc1cc(Cl)c(I)cc1Cl,LFVLUOAHQIVABZ-UHFFFAOYSA-N,LFVLUOAHQIVABZ-UHFFFAOYSA-N,-6.62,,-0.0,0,0.0,-6.62,0.0
+159,4-Chlorotoluene,-3.297,1,126.586,0,1,0,0.0,-3.08,Cc1ccc(Cl)cc1,Cc1ccc(Cl)cc1,NPDACUSDTOMAMK-UHFFFAOYSA-N,NPDACUSDTOMAMK-UHFFFAOYSA-N,-3.08,,-0.0,0,0.0,-3.08,0.0
+160,Metribuzin,-2.324,1,214.294,1,1,1,73.8,-2.253,CSc1nnc(c(=O)n1N)C(C)(C)C,CSc1nnc(C(C)(C)C)c(=O)n1N,FOXFZRUHNHCZPX-UHFFFAOYSA-N,FOXFZRUHNHCZPX-UHFFFAOYSA-N,-2.253,,-0.0,0,1.0,-2.253,1.0
+161,Tricresyl phosphate,-6.39,1,368.3690000000001,0,3,6,44.760000000000005,-6.01,Cc1ccc(OP(=O)(Oc2cccc(C)c2)Oc3ccccc3C)cc1,Cc1ccc(OP(=O)(Oc2cccc(C)c2)Oc2ccccc2C)cc1,FSNFYCPCXFQJSI-UHFFFAOYSA-N,FSNFYCPCXFQJSI-UHFFFAOYSA-N,-6.01,,-0.0,0,0.0,-6.01,0.0
+162,Caproaldehyde,-1.457,1,100.161,0,0,4,17.07,-1.3,CCCCCC=O,CCCCCC=O,JARKCYVAAOWBJS-UHFFFAOYSA-N,JARKCYVAAOWBJS-UHFFFAOYSA-N,-1.3,,-0.0,0,1.0,-1.3,1.0
+163,Butamben,-3.039,1,193.246,1,1,4,52.32,-3.082,CCCCOC(=O)c1ccc(N)cc1,CCCCOC(=O)c1ccc(N)cc1,IUWVALYLNVXWKX-UHFFFAOYSA-N,IUWVALYLNVXWKX-UHFFFAOYSA-N,-3.082,,-0.0,0,0.0,-3.082,0.0
+164,RTI 3,-3.049,1,255.277,1,3,0,68.45,-3.043,O2c1cc(C)ccc1N(C)C(=O)c3cc(N)cnc23 ,Cc1ccc2c(c1)Oc1ncc(N)cc1C(=O)N2C,MUKSMBAVCDPKRC-UHFFFAOYSA-N,MUKSMBAVCDPKRC-UHFFFAOYSA-N,-3.043,,-0.0,0,1.0,-3.043,1.0
+165,Nerol,-2.603,1,154.253,1,0,4,20.23,-2.46,CC(C)=CCC/C(C)=C\CO,CC(C)=CCC/C(C)=C\CO,GLZPCOQZEFWAFX-YFHOEESVSA-N,GLZPCOQZEFWAFX-YFHOEESVSA-N,-2.46,,-0.0,0,1.0,-2.46,1.0
+166,"2,4'-PCB",-5.142,1,223.102,0,2,1,0.0,-5.28,Clc1ccc(cc1)c2ccccc2Cl ,Clc1ccc(-c2ccccc2Cl)cc1,UFNIBRDIUNVOMX-UHFFFAOYSA-N,UFNIBRDIUNVOMX-UHFFFAOYSA-N,-5.28,,-0.0,0,0.0,-5.28,0.0
+167,3-Octanoyloxymethylphenytoin,-4.84,1,408.498,1,3,10,75.71,-6.523,O=C1N(COC(=O)CCCCCCC)C(=O)C(N1)(c2ccccc2)c3ccccc3,CCCCCCCC(=O)OCN1C(=O)NC(c2ccccc2)(c2ccccc2)C1=O,IDZYLIZKJFAXAS-UHFFFAOYSA-N,IDZYLIZKJFAXAS-UHFFFAOYSA-N,-6.523,,-0.0,0,0.0,-6.523,0.0
+168,Nitroethane,-0.462,1,75.067,0,0,1,43.14,-0.22,CCN(=O)=O,CC[N+](=O)[O-],MCSAJNNLRCFZED-UHFFFAOYSA-N,MCSAJNNLRCFZED-UHFFFAOYSA-N,-0.22,,-0.0,0,1.0,-0.22,1.0
+169,Ethalfluralin,-5.063,1,333.266,0,1,6,89.51999999999998,-6.124,CCN(CC(C)=C)c1c(cc(cc1N(=O)=O)C(F)(F)F)N(=O)=O,C=C(C)CN(CC)c1c([N+](=O)[O-])cc(C(F)(F)F)cc1[N+](=O)[O-],PTFJIKYUEPWBMS-UHFFFAOYSA-N,PTFJIKYUEPWBMS-UHFFFAOYSA-N,-6.124,,-0.0,0,0.0,-6.124,0.0
+170,"1,2,3,4-Tetrachlorobenzene",-4.546,1,215.894,0,1,0,0.0,-4.57,Clc1ccc(Cl)c(Cl)c1Cl,Clc1ccc(Cl)c(Cl)c1Cl,GBDZXPJXOMHESU-UHFFFAOYSA-N,GBDZXPJXOMHESU-UHFFFAOYSA-N,-4.57,,-0.0,0,0.0,-4.57,0.0
+171,Meprobamate,-1.376,1,218.253,2,0,6,104.64,-1.807,CCCC(C)(COC(N)=O)COC(N)=O ,CCCC(C)(COC(N)=O)COC(N)=O,NPPQSCRMBWNHMW-UHFFFAOYSA-N,NPPQSCRMBWNHMW-UHFFFAOYSA-N,-1.807,,-0.0,0,1.0,-1.807,1.0
+172,pregnenolone,-4.342,1,316.48500000000007,1,4,1,37.3,-4.65,CC(=O)C3CCC4C2CC=C1CC(O)CCC1(C)C2CCC34C ,CC(=O)C1CCC2C3CC=C4CC(O)CCC4(C)C3CCC12C,ORNBQBCIOKFOEO-UHFFFAOYSA-N,ORNBQBCIOKFOEO-UHFFFAOYSA-N,-4.65,,-0.0,0,0.0,-4.65,0.0
+173,Iodomethane,-1.646,1,141.939,0,0,0,0.0,-1.0,CI,CI,INQOMBQAUSQDDS-UHFFFAOYSA-N,INQOMBQAUSQDDS-UHFFFAOYSA-N,-1.0,,-0.0,0,1.0,-1.0,1.0
+174,cycloheximide,-1.5319999999999998,1,281.35200000000003,2,2,3,83.47,-1.13,CC1CC(C)C(=O)C(C1)C(O)CC2CC(=O)NC(=O)C2 ,CC1CC(C)C(=O)C(C(O)CC2CC(=O)NC(=O)C2)C1,YPHMISFOHDHNIV-UHFFFAOYSA-N,YPHMISFOHDHNIV-UHFFFAOYSA-N,-1.13,,-0.0,0,1.0,-1.13,1.0
+175,3-Heptanoyloxymethylphenytoin,-4.496,1,394.471,1,3,9,75.71,-6.301,O=C1N(COC(=O)CCCCCC)C(=O)C(N1)(c2ccccc2)c3ccccc3,CCCCCCC(=O)OCN1C(=O)NC(c2ccccc2)(c2ccccc2)C1=O,FFGKFDRZZAJYCH-UHFFFAOYSA-N,FFGKFDRZZAJYCH-UHFFFAOYSA-N,-6.301,,-0.0,0,0.0,-6.301,0.0
+176,isophorone,-2.015,1,138.20999999999998,0,1,0,17.07,-1.06,CC1=CC(=O)CC(C)(C)C1 ,CC1=CC(=O)CC(C)(C)C1,HJOVHMDZYOCNQW-UHFFFAOYSA-N,HJOVHMDZYOCNQW-UHFFFAOYSA-N,-1.06,,-0.0,0,1.0,-1.06,1.0
+177,Butabarbital,-1.958,1,212.249,2,1,3,75.27000000000001,-2.39,O=C1NC(=O)NC(=O)C1(CC)C(C)CC,CCC(C)C1(CC)C(=O)NC(=O)NC1=O,ZRIHAIZYIMGOAB-UHFFFAOYSA-N,ZRIHAIZYIMGOAB-UHFFFAOYSA-N,-2.39,,-0.0,0,1.0,-2.39,1.0
+178,5-Nonanone,-2.329,1,142.242,0,0,6,17.07,-2.58,CCCCC(=O)CCCC,CCCCC(=O)CCCC,WSGCRAOTEDLMFQ-UHFFFAOYSA-N,WSGCRAOTEDLMFQ-UHFFFAOYSA-N,-2.58,,-0.0,0,1.0,-2.58,1.0
+179,Glutethimide,-2.591,1,217.268,1,2,2,46.17,-2.337,CCC1(CCC(=O)NC1=O)c2ccccc2 ,CCC1(c2ccccc2)CCC(=O)NC1=O,JMBQKKAJIKAWKF-UHFFFAOYSA-N,JMBQKKAJIKAWKF-UHFFFAOYSA-N,-2.337,,-0.0,0,1.0,-2.337,1.0
+180,3-Methylpentane,-2.6,1,86.178,0,0,2,0.0,-3.68,CCC(C)CC,CCC(C)CC,PFEOZHBOMNWTJB-UHFFFAOYSA-N,PFEOZHBOMNWTJB-UHFFFAOYSA-N,-3.68,,-0.0,0,0.0,-3.68,0.0
+181,Etofenprox,-6.896,1,376.496,0,3,9,27.69,-8.6,CCOc1ccc(cc1)C(C)(C)COCc3cccc(Oc2ccccc2)c3,CCOc1ccc(C(C)(C)COCc2cccc(Oc3ccccc3)c2)cc1,YREQHYQNNWYQCJ-UHFFFAOYSA-N,YREQHYQNNWYQCJ-UHFFFAOYSA-N,-8.6,,-0.0,0,0.0,-8.6,0.0
+182,Methaqualone,-3.881,1,250.301,0,3,1,34.89,-2.925,Cc1ccccc1n3c(C)nc2ccccc2c3=O,Cc1ccccc1-n1c(C)nc2ccccc2c1=O,JEYCTXHKTXCGPB-UHFFFAOYSA-N,JEYCTXHKTXCGPB-UHFFFAOYSA-N,-2.925,,-0.0,0,1.0,-2.925,1.0
+183,Chloroacetonitrile,-0.4479999999999999,1,75.498,0,0,0,23.79,-0.092,ClCC#N,N#CCCl,RENMDAKOXSCIGH-UHFFFAOYSA-N,RENMDAKOXSCIGH-UHFFFAOYSA-N,-0.092,,-0.0,0,1.0,-0.092,1.0
+184,Trichloronate,-5.225,1,333.60400000000004,0,1,5,18.46,-5.752000000000001,CCOP(=S)(CC)Oc1cc(Cl)c(Cl)cc1Cl,CCOP(=S)(CC)Oc1cc(Cl)c(Cl)cc1Cl,ANIAQSUBRGXWLS-UHFFFAOYSA-N,ANIAQSUBRGXWLS-UHFFFAOYSA-N,-5.752000000000001,,-0.0,0,0.0,-5.752000000000001,0.0
+185,Ethisterone,-3.858,1,312.45300000000003,1,4,0,37.3,-5.66,CC12CCC(=O)C=C1CCC3C2CCC4(C)C3CCC4(O)C#C ,C#CC1(O)CCC2C3CCC4=CC(=O)CCC4(C)C3CCC21C,CHNXZKVNWQUJIB-UHFFFAOYSA-N,CHNXZKVNWQUJIB-UHFFFAOYSA-N,-5.66,,-0.0,0,0.0,-5.66,0.0
+186,Pyridazine,-0.619,2,80.08999999999999,0,1,0,25.78,1.1,c1ccnnc1,c1ccnnc1,PBMFSQRYOILNGV-UHFFFAOYSA-N,PBMFSQRYOILNGV-UHFFFAOYSA-N,1.1,,0.0,0,1.0,1.1,1.0
+187,"1,2,3,5-Tetrachlorobenzene",-4.621,1,215.894,0,1,0,0.0,-4.63,Clc1cc(Cl)c(Cl)c(Cl)c1,Clc1cc(Cl)c(Cl)c(Cl)c1,QZYNWJQFTJXIRN-UHFFFAOYSA-N,QZYNWJQFTJXIRN-UHFFFAOYSA-N,-4.63,,-0.0,0,0.0,-4.63,0.0
+188,Diosgenin,-5.681,1,414.63000000000017,1,6,0,38.69,-7.32,C1C(O)CCC2(C)CC3CCC4(C)C5(C)CC6OCC(C)CC6OC5CC4C3C=C21,CC1COC2CC3(C)C(CC4C5C=C6CC(O)CCC6(C)CC5CCC43C)OC2C1,CPMZWWWXIWHTMU-UHFFFAOYSA-N,CPMZWWWXIWHTMU-UHFFFAOYSA-N,-7.32,,-0.0,0,0.0,-7.32,0.0
+189,o-Aminophenol,-1.465,1,109.128,2,1,0,46.25,-0.72,Nc1ccccc1O,Nc1ccccc1O,CDAWCLOXVUBKRW-UHFFFAOYSA-N,CDAWCLOXVUBKRW-UHFFFAOYSA-N,-0.72,,-0.0,0,1.0,-0.72,1.0
+190,Ethyl nonanoate,-3.3160000000000003,1,186.295,0,0,8,26.3,-3.8,CCCCCCCCC(=O)OCC,CCCCCCCCC(=O)OCC,BYEVBITUADOIGY-UHFFFAOYSA-N,BYEVBITUADOIGY-UHFFFAOYSA-N,-3.8,,-0.0,0,0.0,-3.8,0.0
+191,metalaxyl,-2.87,1,279.336,0,1,5,55.84,-1.601,COCC(=O)N(C(C)C(=O)OC)c1c(C)cccc1C ,COCC(=O)N(c1c(C)cccc1C)C(C)C(=O)OC,ZQEIXNIJLIKNTD-UHFFFAOYSA-N,ZQEIXNIJLIKNTD-UHFFFAOYSA-N,-1.601,,-0.0,0,1.0,-1.601,1.0
+192,Propoxur,-2.4090000000000003,1,209.245,1,1,3,47.56,-2.05,CNC(=O)Oc1ccccc1OC(C)C,CNC(=O)Oc1ccccc1OC(C)C,ISRUGXGCCGIOQO-UHFFFAOYSA-N,ISRUGXGCCGIOQO-UHFFFAOYSA-N,-2.05,,-0.0,0,1.0,-2.05,1.0
+193,2-Chlorobutane,-1.94,1,92.569,0,0,1,0.0,-1.96,CCC(C)Cl,CCC(C)Cl,BSPCSKHALVHRSR-UHFFFAOYSA-N,BSPCSKHALVHRSR-UHFFFAOYSA-N,-1.96,,-0.0,0,1.0,-1.96,1.0
+194,2-Napthol,-3.08,1,144.17299999999997,1,2,0,20.23,-2.28,Oc1ccc2ccccc2c1,Oc1ccc2ccccc2c1,JWAZRIHNYRIHIV-UHFFFAOYSA-N,JWAZRIHNYRIHIV-UHFFFAOYSA-N,-2.28,,-0.0,0,1.0,-2.28,1.0
+195,Oxadiazon,-5.265,1,345.22600000000017,0,2,3,57.26,-5.696000000000001,CC(C)Oc1cc(c(Cl)cc1Cl)n2nc(oc2=O)C(C)(C)C,CC(C)Oc1cc(-n2nc(C(C)(C)C)oc2=O)c(Cl)cc1Cl,CHNUNORXWHYHNE-UHFFFAOYSA-N,CHNUNORXWHYHNE-UHFFFAOYSA-N,-5.696000000000001,,-0.0,0,0.0,-5.696000000000001,0.0
+196,1-Hexyne ,-1.801,1,82.14599999999999,0,0,2,0.0,-2.36,CCCCC#C,C#CCCCC,CGHIBGNXEGJPQZ-UHFFFAOYSA-N,CGHIBGNXEGJPQZ-UHFFFAOYSA-N,-2.36,,-0.0,0,1.0,-2.36,1.0
+197,1-Nonyne ,-2.864,1,124.227,0,0,5,0.0,-4.24,CCCCCCCC#C,C#CCCCCCCC,OSSQSXOTMIGBCF-UHFFFAOYSA-N,OSSQSXOTMIGBCF-UHFFFAOYSA-N,-4.24,,-0.0,0,0.0,-4.24,0.0
+198,2-Chlorotoluene,-3.297,1,126.586,0,1,0,0.0,-3.52,Cc1ccccc1Cl,Cc1ccccc1Cl,IBSQPLPBRSHTTG-UHFFFAOYSA-N,IBSQPLPBRSHTTG-UHFFFAOYSA-N,-3.52,,-0.0,0,0.0,-3.52,0.0
+199,Diisopropyl ether ,-1.281,1,102.177,0,0,2,9.23,-1.1,CC(C)OC(C)C,CC(C)OC(C)C,ZAFNJMIOTHYJRJ-UHFFFAOYSA-N,ZAFNJMIOTHYJRJ-UHFFFAOYSA-N,-1.1,,-0.0,0,1.0,-1.1,1.0
+200,Dapsone,-2.464,1,248.307,2,2,2,86.18,-3.094,Nc1ccc(cc1)S(=O)(=O)c2ccc(N)cc2,Nc1ccc(S(=O)(=O)c2ccc(N)cc2)cc1,MQJKPEGWNLWLTK-UHFFFAOYSA-N,MQJKPEGWNLWLTK-UHFFFAOYSA-N,-3.094,,-0.0,0,0.0,-3.094,0.0
+201,Methyl hydrazine,0.5429999999999999,1,46.073,2,0,0,38.05,1.34,CNN,CNN,HDZGCSFEDULWCS-UHFFFAOYSA-N,HDZGCSFEDULWCS-UHFFFAOYSA-N,1.34,,0.0,0,1.0,1.34,1.0
+202,Propyne,-0.672,1,40.065000000000005,0,0,0,0.0,-0.41,CC#C,C#CC,MWWATHDPGQKSAR-UHFFFAOYSA-N,MWWATHDPGQKSAR-UHFFFAOYSA-N,-0.41,,-0.0,0,1.0,-0.41,1.0
+203,Phoxim,-4.557,1,298.304,0,1,7,63.84,-4.862,CCOP(=S)(OCC)ON=C(C#N)c1ccccc1,CCOP(=S)(OCC)ON=C(C#N)c1ccccc1,ATROHALUCMTWTB-UHFFFAOYSA-N,ATROHALUCMTWTB-UHFFFAOYSA-N,-4.862,,-0.0,0,0.0,-4.862,0.0
+204,Propetamphos,-2.826,1,281.314,1,0,7,56.790000000000006,-3.408,CCNP(=S)(OC)OC(=CC(=O)OC(C)C)C,CCNP(=S)(OC)OC(C)=CC(=O)OC(C)C,BZNDWPRGXNILMS-UHFFFAOYSA-N,BZNDWPRGXNILMS-UHFFFAOYSA-N,-3.408,,-0.0,0,0.0,-3.408,0.0
+205,Acrolein,-0.184,1,56.064,0,0,1,17.07,0.57,C=CC=O,C=CC=O,HGINCPLSRVDWNT-UHFFFAOYSA-N,HGINCPLSRVDWNT-UHFFFAOYSA-N,0.57,,0.0,0,1.0,0.57,1.0
+206,Hypoxanthine,-0.6559999999999999,1,136.114,2,2,0,74.43,-2.296,O=c1[nH]cnc2nc[nH]c12 ,O=c1[nH]cnc2nc[nH]c12,FDGQSTZJBFJUBT-UHFFFAOYSA-N,FDGQSTZJBFJUBT-UHFFFAOYSA-N,-2.296,,-0.0,0,1.0,-2.296,1.0
+207,6-hydroxyquinoline,-2.725,1,145.161,1,2,0,33.120000000000005,-2.16,Oc2ccc1ncccc1c2 ,Oc1ccc2ncccc2c1,OVYWMEWYEJLIER-UHFFFAOYSA-N,OVYWMEWYEJLIER-UHFFFAOYSA-N,-2.16,,-0.0,0,1.0,-2.16,1.0
+208,Fluorobenzene,-2.514,1,96.10399999999998,0,1,0,0.0,-1.8,Fc1ccccc1,Fc1ccccc1,PYLWMHQQBFSUBP-UHFFFAOYSA-N,PYLWMHQQBFSUBP-UHFFFAOYSA-N,-1.8,,-0.0,0,1.0,-1.8,1.0
+209,1-Chloropropane,-1.585,1,78.542,0,0,1,0.0,-1.47,CCCCl,CCCCl,SNMVRZFUUCLYTO-UHFFFAOYSA-N,SNMVRZFUUCLYTO-UHFFFAOYSA-N,-1.47,,-0.0,0,1.0,-1.47,1.0
+210,Ethyl acetate,-0.77,1,88.106,0,0,1,26.3,-0.04,CCOC(=O)C,CCOC(C)=O,XEKOWRVHYACXOJ-UHFFFAOYSA-N,XEKOWRVHYACXOJ-UHFFFAOYSA-N,-0.04,,-0.0,0,1.0,-0.04,1.0
+211,"2,2-Dimethylpentane",-2.938,1,100.20499999999998,0,0,1,0.0,-4.36,CCCC(C)(C)C,CCCC(C)(C)C,CXOWYJMDMMMMJO-UHFFFAOYSA-N,CXOWYJMDMMMMJO-UHFFFAOYSA-N,-4.36,,-0.0,0,0.0,-4.36,0.0
+212,Pentamethylbenzene,-3.993,1,148.249,0,1,0,0.0,-4.0,Cc1cc(C)c(C)c(C)c1C,Cc1cc(C)c(C)c(C)c1C,BEZDDPMMPIDMGJ-UHFFFAOYSA-N,BEZDDPMMPIDMGJ-UHFFFAOYSA-N,-4.0,,-0.0,0,0.0,-4.0,0.0
+213,eucalyptol,-2.579,1,154.253,0,3,0,9.23,-1.64,CC12CCC(CC1)C(C)(C)O2 ,CC12CCC(CC1)C(C)(C)O2,WEEGYLXZBRQIMU-UHFFFAOYSA-N,WEEGYLXZBRQIMU-UHFFFAOYSA-N,-1.69,0.0707106781186548,-2.9585798816568074,0,1.0,-1.64,1.0
+214,dibutyl sebacate,-4.726,1,314.46600000000007,0,0,15,52.60000000000001,-3.896,CCCCOC(=O)CCCCCCCCC(=O)OCCCC,CCCCOC(=O)CCCCCCCCC(=O)OCCCC,PYGXAGIECVVIOZ-UHFFFAOYSA-N,PYGXAGIECVVIOZ-UHFFFAOYSA-N,-3.896,,-0.0,0,0.0,-3.896,0.0
+215,"4,4'-PCB",-5.299,1,223.102,0,2,1,0.0,-6.56,Clc1ccc(cc1)c2ccc(Cl)cc2 ,Clc1ccc(-c2ccc(Cl)cc2)cc1,YTBRNEUEFCNVHC-UHFFFAOYSA-N,YTBRNEUEFCNVHC-UHFFFAOYSA-N,-6.56,,-0.0,0,0.0,-6.56,0.0
+216,"2,3-Dimethylpyridine",-2.067,1,107.156,0,1,0,12.89,0.38,Cc1cccnc1C,Cc1cccnc1C,HPYNZHMRTTWQTB-UHFFFAOYSA-N,HPYNZHMRTTWQTB-UHFFFAOYSA-N,0.38,,0.0,0,1.0,0.38,1.0
+217,Carvone,-2.042,1,150.22099999999998,0,1,1,17.07,-2.06,CC(=C)C1CC=C(C)C(=O)C1,C=C(C)C1CC=C(C)C(=O)C1,ULDHMXUKGWMISQ-UHFFFAOYSA-N,ULDHMXUKGWMISQ-UHFFFAOYSA-N,-2.06,,-0.0,0,1.0,-2.06,1.0
+218,Carbophenthion,-5.827999999999999,1,342.875,0,1,8,18.46,-5.736000000000001,CCOP(=S)(OCC)SCSc1ccc(Cl)cc1,CCOP(=S)(OCC)SCSc1ccc(Cl)cc1,VEDTXTNSFWUXGQ-UHFFFAOYSA-N,VEDTXTNSFWUXGQ-UHFFFAOYSA-N,-5.736000000000001,,-0.0,0,0.0,-5.736000000000001,0.0
+219,"Etoposide (148-167,25mg/ml)",-3.292,1,588.5620000000001,3,7,5,160.83,-3.571,COc1cc(cc(OC)c1O)C6C2C(COC2=O)C(OC4OC3COC(C)OC3C(O)C4O)c7cc5OCOc5cc67,COc1cc(C2c3cc4c(cc3C(OC3OC5COC(C)OC5C(O)C3O)C3COC(=O)C23)OCO4)cc(OC)c1O,VJJPUSNTGOMMGY-UHFFFAOYSA-N,VJJPUSNTGOMMGY-UHFFFAOYSA-N,-3.571,,-0.0,0,0.0,-3.571,0.0
+220,Perylene,-6.007000000000001,2,252.316,0,5,0,0.0,-8.804,c1cc2cccc3c4cccc5cccc(c(c1)c23)c54,c1cc2cccc3c4cccc5cccc(c(c1)c23)c54,CSHWQDPOILHKBI-UHFFFAOYSA-N,CSHWQDPOILHKBI-UHFFFAOYSA-N,-8.804,,-0.0,0,0.0,-8.804,0.0
+221,"2,4-Dinitrotoluene",-2.604,1,182.135,0,1,2,86.28,-2.82,Cc1ccc(cc1N(=O)=O)N(=O)=O,Cc1ccc([N+](=O)[O-])cc1[N+](=O)[O-],RMBFBMJGBANMMK-UHFFFAOYSA-N,RMBFBMJGBANMMK-UHFFFAOYSA-N,-2.82,,-0.0,0,1.0,-2.82,1.0
+222,2-bromonaphthalene,-4.434,1,207.07,0,2,0,0.0,-4.4,c1c(Br)ccc2ccccc12 ,Brc1ccc2ccccc2c1,APSMUYYLXZULMS-UHFFFAOYSA-N,APSMUYYLXZULMS-UHFFFAOYSA-N,-4.4,0.0,-0.0,0,0.0,-4.4,0.0
+223,Formetanate,-1.846,1,221.26,1,1,3,53.93,-2.34,CNC(=O)Oc1cccc(N=CN(C)C)c1,CNC(=O)Oc1cccc(N=CN(C)C)c1,RMFNNCGOSPBBAD-UHFFFAOYSA-N,RMFNNCGOSPBBAD-UHFFFAOYSA-N,-2.34,,-0.0,0,1.0,-2.34,1.0
+224,6-methoxypteridine,-1.589,1,162.15200000000002,0,2,1,60.790000000000006,-1.139,COc2cnc1ncncc1n2,COc1cnc2ncncc2n1,AWIJPALBOQNRBS-UHFFFAOYSA-N,AWIJPALBOQNRBS-UHFFFAOYSA-N,-1.139,,-0.0,0,1.0,-1.139,1.0
+225,nevirapine,-3.397,1,266.30400000000003,1,4,1,58.120000000000005,-3.19,Cc3ccnc4N(C1CC1)c2ncccc2C(=O)Nc34 ,Cc1ccnc2c1NC(=O)c1cccnc1N2C1CC1,NQDJXKOVJZTUJA-UHFFFAOYSA-N,NQDJXKOVJZTUJA-UHFFFAOYSA-N,-3.19,,-0.0,0,0.0,-3.19,0.0
+226,Isazofos,-3.76,1,313.747,0,1,7,58.4,-3.658,CCOP(=S)(OCC)Oc1nc(Cl)n(n1)C(C)C,CCOP(=S)(OCC)Oc1nc(Cl)n(C(C)C)n1,XRHGWAGWAHHFLF-UHFFFAOYSA-N,XRHGWAGWAHHFLF-UHFFFAOYSA-N,-3.658,,-0.0,0,0.0,-3.658,0.0
+227,"2-Methyl-1,3-Butadiene ",-1.714,1,68.11900000000001,0,0,1,0.0,-2.03,CC(=C)C=C,C=CC(=C)C,RRHGJUQNOFWUDK-UHFFFAOYSA-N,RRHGJUQNOFWUDK-UHFFFAOYSA-N,-2.03,,-0.0,0,1.0,-2.03,1.0
+228,linalool,-2.399,1,154.253,1,0,4,20.23,-1.99,CC(C)=CCCC(O)(C)C=C,C=CC(C)(O)CCC=C(C)C,CDOSHBSSFJOMGT-UHFFFAOYSA-N,CDOSHBSSFJOMGT-UHFFFAOYSA-N,-1.99,,-0.0,0,1.0,-1.99,1.0
+229,Fenthion,-4.265,1,278.335,0,1,5,27.69,-4.57,COP(=S)(OC)Oc1ccc(SC)c(C)c1,COP(=S)(OC)Oc1ccc(SC)c(C)c1,PNVJTZOFSHSLTO-UHFFFAOYSA-N,PNVJTZOFSHSLTO-UHFFFAOYSA-N,-4.57,,-0.0,0,0.0,-4.57,0.0
+230,Cyclohexanol ,-1.261,1,100.161,1,1,0,20.23,-0.44,OC1CCCCC1,OC1CCCCC1,HPXRVTGHNJAIIH-UHFFFAOYSA-N,HPXRVTGHNJAIIH-UHFFFAOYSA-N,-0.44,,-0.0,0,1.0,-0.44,1.0
+231,5-Allyl-5-methylbarbital,-1.013,1,182.179,2,1,2,75.27000000000001,-1.16,O=C1NC(=O)NC(=O)C1(C)CC=C,C=CCC1(C)C(=O)NC(=O)NC1=O,WMSQREUDUHHDDC-UHFFFAOYSA-N,WMSQREUDUHHDDC-UHFFFAOYSA-N,-1.16,,-0.0,0,1.0,-1.16,1.0
+232,Epiandrosterone,-3.882,1,290.447,1,4,0,37.3,-4.16,CC34CCC1C(CCC2CC(O)CCC12C)C3CCC4=O,CC12CCC3C(CCC4CC(O)CCC43C)C1CCC2=O,QGXBDMJGAMFCBF-UHFFFAOYSA-N,QGXBDMJGAMFCBF-UHFFFAOYSA-N,-4.281000000000001,0.1711198410471441,-2.826442419995338,0,0.0,-4.16,0.0
+233,mannitol,0.647,1,182.172,6,0,5,121.38,0.06,OCC(O)C(O)C(O)C(O)CO ,OCC(O)C(O)C(O)C(O)CO,FBPFZTCFMRRESA-UHFFFAOYSA-N,FBPFZTCFMRRESA-UHFFFAOYSA-N,0.5750000000000001,0.728319984622144,89.56521739130436,0,1.0,0.06,1.0
+234,4-Methylbiphenyl,-4.424,1,168.239,0,2,1,0.0,-4.62,Cc1ccc(cc1)c2ccccc2,Cc1ccc(-c2ccccc2)cc1,ZZLCFHIKESPLTH-UHFFFAOYSA-N,ZZLCFHIKESPLTH-UHFFFAOYSA-N,-4.62,,-0.0,0,0.0,-4.62,0.0
+235,Atrazine,-3.069,1,215.688,2,1,4,62.73,-3.85,CCNc1nc(Cl)nc(NC(C)C)n1,CCNc1nc(Cl)nc(NC(C)C)n1,MXWJVTOOROXGIU-UHFFFAOYSA-N,MXWJVTOOROXGIU-UHFFFAOYSA-N,-3.85,,-0.0,0,0.0,-3.85,0.0
+236,Phenylthiourea,-1.7009999999999998,1,152.22199999999998,2,1,1,38.05,-1.77,NC(=S)Nc1ccccc1,NC(=S)Nc1ccccc1,FULZLIGZKMKICU-UHFFFAOYSA-N,FULZLIGZKMKICU-UHFFFAOYSA-N,-1.77,,-0.0,0,1.0,-1.77,1.0
+237,4-Heptanone,-1.62,1,114.188,0,0,4,17.07,-1.3,CCCC(=O)CCC,CCCC(=O)CCC,HCFAJYNVAYBARA-UHFFFAOYSA-N,HCFAJYNVAYBARA-UHFFFAOYSA-N,-1.3,,-0.0,0,1.0,-1.3,1.0
+238,"3,3-Dimethyl-2-butanone",-1.25,1,100.161,0,0,0,17.07,-0.72,CC(=O)C(C)(C)C,CC(=O)C(C)(C)C,PJGSXYOJTGTZAV-UHFFFAOYSA-N,PJGSXYOJTGTZAV-UHFFFAOYSA-N,-0.72,,-0.0,0,1.0,-0.72,1.0
+239,4-Chlorophenol ,-2.761,1,128.558,1,1,0,20.23,-0.7,Oc1ccc(Cl)cc1,Oc1ccc(Cl)cc1,WXNZTHHGJRFXKQ-UHFFFAOYSA-N,WXNZTHHGJRFXKQ-UHFFFAOYSA-N,-0.7,,-0.0,0,1.0,-0.7,1.0
+240,Cyclohexanone,-0.996,1,98.145,0,1,0,17.07,-0.6,O=C1CCCCC1,O=C1CCCCC1,JHIVVAPYMSGYDF-UHFFFAOYSA-N,JHIVVAPYMSGYDF-UHFFFAOYSA-N,-0.6,,-0.0,0,1.0,-0.6,1.0
+241,m-Methylaniline,-1.954,1,107.156,1,1,0,26.02,-0.85,Cc1cccc(N)c1,Cc1cccc(N)c1,JJYPMNFTHPTTDI-UHFFFAOYSA-N,JJYPMNFTHPTTDI-UHFFFAOYSA-N,-0.85,,-0.0,0,1.0,-0.85,1.0
+242,Trichloroacetonitrile,-2.019,1,144.388,0,0,0,23.79,-2.168,ClC(Cl)(Cl)C#N,N#CC(Cl)(Cl)Cl,DRUIESSIVFYOMK-UHFFFAOYSA-N,DRUIESSIVFYOMK-UHFFFAOYSA-N,-2.168,,-0.0,0,1.0,-2.168,1.0
+243,norflurazon,-4.029,1,303.67100000000005,1,2,2,46.92,-4.046,CNc2cnn(c1cccc(c1)C(F)(F)F)c(=O)c2Cl,CNc1cnn(-c2cccc(C(F)(F)F)c2)c(=O)c1Cl,NVGOPFQZYCNLDU-UHFFFAOYSA-N,NVGOPFQZYCNLDU-UHFFFAOYSA-N,-4.046,,-0.0,0,0.0,-4.046,0.0
+244,2-Decanone,-2.617,1,156.269,0,0,7,17.07,-3.3,CCCCCCCCC(=O)C,CCCCCCCCC(C)=O,ZAJNGDIORYACQU-UHFFFAOYSA-N,ZAJNGDIORYACQU-UHFFFAOYSA-N,-3.3,,-0.0,0,0.0,-3.3,0.0
+245,Ipazine,-3.497,1,243.742,1,1,5,53.940000000000005,-3.785,CCN(CC)c1nc(Cl)nc(NC(C)C)n1,CCN(CC)c1nc(Cl)nc(NC(C)C)n1,OWYWGLHRNBIFJP-UHFFFAOYSA-N,OWYWGLHRNBIFJP-UHFFFAOYSA-N,-3.785,,-0.0,0,0.0,-3.785,0.0
+246,Benzocaine,-2.383,1,165.19199999999998,1,1,2,52.32,-2.616,CCOC(=O)c1ccc(N)cc1,CCOC(=O)c1ccc(N)cc1,BLFLLBZGZJTVJG-UHFFFAOYSA-N,BLFLLBZGZJTVJG-UHFFFAOYSA-N,-2.616,,-0.0,0,1.0,-2.616,1.0
+247,"1,2,4-Trichlorobenzene",-4.083,1,181.449,0,1,0,0.0,-3.59,Clc1ccc(Cl)c(Cl)c1,Clc1ccc(Cl)c(Cl)c1,PBKONEOXTCPAFI-UHFFFAOYSA-N,PBKONEOXTCPAFI-UHFFFAOYSA-N,-3.59,,-0.0,0,0.0,-3.59,0.0
+248,Triazolam,-3.948,1,343.2170000000001,0,4,1,43.07,-4.09,Cc3nnc4CN=C(c1ccccc1Cl)c2cc(Cl)ccc2n34,Cc1nnc2n1-c1ccc(Cl)cc1C(c1ccccc1Cl)=NC2,JOFWLTCLBGQGBO-UHFFFAOYSA-N,JOFWLTCLBGQGBO-UHFFFAOYSA-N,-4.09,,-0.0,0,0.0,-4.09,0.0
+249,"1,2-Benzenediol",-1.635,1,110.11199999999998,2,1,0,40.46,0.62,Oc1ccccc1O,Oc1ccccc1O,YCIMNLLNPGFGHC-UHFFFAOYSA-N,YCIMNLLNPGFGHC-UHFFFAOYSA-N,0.62,,0.0,0,1.0,0.62,1.0
+250,Reverse Transcriptase inhibitor 1,-2.794,1,254.293,0,3,1,49.330000000000005,-2.62,CCN2c1ncccc1N(C)C(=O)c3cccnc23 ,CCN1c2ncccc2C(=O)N(C)c2cccnc21,SVFKQHLOBRUZGV-UHFFFAOYSA-N,SVFKQHLOBRUZGV-UHFFFAOYSA-N,-2.62,,-0.0,0,1.0,-2.62,1.0
+251,Dimethyl sulfide,-0.758,1,62.137,0,0,0,0.0,-0.45,CSC,CSC,QMMFVYPAHWMCMS-UHFFFAOYSA-N,QMMFVYPAHWMCMS-UHFFFAOYSA-N,-0.45,,-0.0,0,1.0,-0.45,1.0
+252,2-Bromotoluene,-3.667,1,171.03699999999998,0,1,0,0.0,-2.23,Cc1ccccc1Br,Cc1ccccc1Br,QSSXJPIWXQTSIX-UHFFFAOYSA-N,QSSXJPIWXQTSIX-UHFFFAOYSA-N,-2.23,,-0.0,0,1.0,-2.23,1.0
+253,O-Ethyl carbamate,-0.218,1,89.09400000000001,1,0,1,52.32,0.85,CCOC(=O)N,CCOC(N)=O,JOYRKODLDBILNP-UHFFFAOYSA-N,JOYRKODLDBILNP-UHFFFAOYSA-N,0.85,,0.0,0,1.0,0.85,1.0
+254,megestrol acetate,-4.417,1,384.5160000000002,0,4,2,60.440000000000005,-5.35,CC(=O)OC3(CCC4C2C=C(C)C1=CC(=O)CCC1(C)C2CCC34C)C(C)=O ,CC(=O)OC1(C(C)=O)CCC2C3C=C(C)C4=CC(=O)CCC4(C)C3CCC21C,RQZAXGRLVPAYTJ-UHFFFAOYSA-N,RQZAXGRLVPAYTJ-UHFFFAOYSA-N,-5.35,,-0.0,0,0.0,-5.35,0.0
+255,"2,4-Dimethyl-3-pentanol",-1.6469999999999998,1,116.20399999999998,1,0,2,20.23,-1.22,CC(C)C(O)C(C)C,CC(C)C(O)C(C)C,BAYAKMPRFGNNFW-UHFFFAOYSA-N,BAYAKMPRFGNNFW-UHFFFAOYSA-N,-1.22,,-0.0,0,1.0,-1.22,1.0
+256,Napthalene,-3.468,2,128.17399999999995,0,2,0,0.0,-3.6,c1ccc2ccccc2c1,c1ccc2ccccc2c1,UFWIBTONFRDIAS-UHFFFAOYSA-N,UFWIBTONFRDIAS-UHFFFAOYSA-N,-3.6,,-0.0,0,0.0,-3.6,0.0
+257,N-Ethylaniline,-2.389,1,121.18299999999996,1,1,2,12.03,-1.7,CCNc1ccccc1,CCNc1ccccc1,OJGMBLNIHDZDGS-UHFFFAOYSA-N,OJGMBLNIHDZDGS-UHFFFAOYSA-N,-1.7,,-0.0,0,1.0,-1.7,1.0
+258,Phenytoin,-3.057,1,252.273,2,3,2,58.2,-4.097,O=C1NC(=O)C(N1)(c2ccccc2)c3ccccc3,O=C1NC(=O)C(c2ccccc2)(c2ccccc2)N1,CXOFVDLJLONNDW-UHFFFAOYSA-N,CXOFVDLJLONNDW-UHFFFAOYSA-N,-4.097,,-0.0,0,0.0,-4.097,0.0
+259,"7,12-Dimethylbenz(a)anthracene",-6.297000000000001,1,256.348,0,4,0,0.0,-7.02,Cc1c2ccccc2c(C)c3ccc4ccccc4c13,Cc1c2ccccc2c(C)c2c1ccc1ccccc12,ARSRBNBHOADGJU-UHFFFAOYSA-N,ARSRBNBHOADGJU-UHFFFAOYSA-N,-7.02,,-0.0,0,0.0,-7.02,0.0
+260,Dialifor,-5.026,1,393.85400000000016,0,2,8,55.84,-6.34,CCOP(=S)(OCC)SC(CCl)N1C(=O)c2ccccc2C1=O,CCOP(=S)(OCC)SC(CCl)N1C(=O)c2ccccc2C1=O,MUMQYXACQUZOFP-UHFFFAOYSA-N,MUMQYXACQUZOFP-UHFFFAOYSA-N,-6.34,0.0,-0.0,0,0.0,-6.34,0.0
+261,Methoxychlor,-5.537999999999999,1,345.6529999999999,0,2,4,18.46,-6.89,COc1ccc(cc1)C(c2ccc(OC)cc2)C(Cl)(Cl)Cl,COc1ccc(C(c2ccc(OC)cc2)C(Cl)(Cl)Cl)cc1,IAKOZHOLGAGEJT-UHFFFAOYSA-N,IAKOZHOLGAGEJT-UHFFFAOYSA-N,-6.89,,-0.0,0,0.0,-6.89,0.0
+262,TEFLUBENZURON,-5.462000000000001,1,381.1120000000001,2,2,2,58.2,-7.28,Fc1cccc(F)c1C(=O)NC(=O)Nc2cc(Cl)c(F)c(Cl)c2F ,O=C(NC(=O)c1c(F)cccc1F)Nc1cc(Cl)c(F)c(Cl)c1F,CJDWRQLODFKPEL-UHFFFAOYSA-N,CJDWRQLODFKPEL-UHFFFAOYSA-N,-7.28,,-0.0,0,0.0,-7.28,0.0
+263,3-Pentanoyloxymethylphenytoin,-3.81,1,366.417,1,3,7,75.71,-4.678,O=C1N(COC(=O)CCCC)C(=O)C(N1)(c2ccccc2)c3ccccc3,CCCCC(=O)OCN1C(=O)NC(c2ccccc2)(c2ccccc2)C1=O,XCEYNQCFXXAOBL-UHFFFAOYSA-N,XCEYNQCFXXAOBL-UHFFFAOYSA-N,-4.678,,-0.0,0,0.0,-4.678,0.0
+264,Monuron,-2.6710000000000003,1,198.653,1,1,1,32.34,-2.89,CN(C)C(=O)Nc1ccc(Cl)cc1,CN(C)C(=O)Nc1ccc(Cl)cc1,BMLIZLVNXIYGCK-UHFFFAOYSA-N,BMLIZLVNXIYGCK-UHFFFAOYSA-N,-2.89,,-0.0,0,1.0,-2.89,1.0
+265,Flutriafol,-3.569,1,301.296,1,3,4,50.94,-3.37,OC(Cn1cncn1)(c2ccc(F)cc2)c3ccccc3F,OC(Cn1cncn1)(c1ccc(F)cc1)c1ccccc1F,JWUCHKBSVLQQCO-UHFFFAOYSA-N,JWUCHKBSVLQQCO-UHFFFAOYSA-N,-3.37,,-0.0,0,0.0,-3.37,0.0
+266,triamcinolone diacetate,-3.876,1,478.51300000000026,2,4,4,127.20000000000002,-4.13,CC(=O)OCC(=O)C3(O)C(CC4C2CCC1=CC(=O)C=CC1(C)C2(F)C(O)CC34C)OC(C)=O ,CC(=O)OCC(=O)C1(O)C(OC(C)=O)CC2C3CCC4=CC(=O)C=CC4(C)C3(F)C(O)CC21C,XGMPVBXKDAHORN-UHFFFAOYSA-N,XGMPVBXKDAHORN-UHFFFAOYSA-N,-4.13,,-0.0,0,0.0,-4.13,0.0
+267,1-Bromobutane,-2.303,1,137.01999999999998,0,0,2,0.0,-2.37,CCCCBr,CCCCBr,MPPPKRYCTPRNTB-UHFFFAOYSA-N,MPPPKRYCTPRNTB-UHFFFAOYSA-N,-2.37,,-0.0,0,1.0,-2.37,1.0
+268,"1,2,4,5-Tetrabromobenzene",-6.001,1,393.698,0,1,0,0.0,-6.98,Brc1cc(Br)c(Br)cc1Br,Brc1cc(Br)c(Br)cc1Br,QCKHVNQHBOGZER-UHFFFAOYSA-N,QCKHVNQHBOGZER-UHFFFAOYSA-N,-6.98,,-0.0,0,0.0,-6.98,0.0
+269,4-Methyl-2-pentanone,-1.1840000000000002,1,100.161,0,0,2,17.07,-0.74,CC(C)CC(=O)C,CC(=O)CC(C)C,NTIZESTWPVYFNL-UHFFFAOYSA-N,NTIZESTWPVYFNL-UHFFFAOYSA-N,-0.74,,-0.0,0,1.0,-0.74,1.0
+270,cycloate,-3.35,1,215.362,0,1,3,20.31,-3.4,CCSC(=O)N(CC)C1CCCCC1 ,CCSC(=O)N(CC)C1CCCCC1,DFCAFRGABIXSDS-UHFFFAOYSA-N,DFCAFRGABIXSDS-UHFFFAOYSA-N,-3.4,,-0.0,0,0.0,-3.4,0.0
+271,4-Chloroanisole,-3.057,1,142.585,0,1,1,9.23,-2.78,COc1ccc(Cl)cc1,COc1ccc(Cl)cc1,YRGAYAGBVIXNAQ-UHFFFAOYSA-N,YRGAYAGBVIXNAQ-UHFFFAOYSA-N,-2.78,,-0.0,0,1.0,-2.78,1.0
+272,Deltamethrin,-7.44,1,505.2060000000002,0,3,6,59.32000000000001,-8.402000000000001,CC1(C)C(C=C(Br)Br)C1C(=O)OC(C#N)c2cccc(Oc3ccccc3)c2,CC1(C)C(C=C(Br)Br)C1C(=O)OC(C#N)c1cccc(Oc2ccccc2)c1,OWZREIFADZCYQD-UHFFFAOYSA-N,OWZREIFADZCYQD-UHFFFAOYSA-N,-8.402000000000001,,-0.0,0,0.0,-8.402000000000001,0.0
+273,Talbutal,-2.06,1,224.26,2,1,4,75.27000000000001,-2.016,CCC(C)C1(CC=C)C(=O)NC(=O)NC1=O,C=CCC1(C(C)CC)C(=O)NC(=O)NC1=O,BJVVMKUXKQHWJK-UHFFFAOYSA-N,BJVVMKUXKQHWJK-UHFFFAOYSA-N,-2.016,,-0.0,0,1.0,-2.016,1.0
+274,Fenitrothion,-3.845,1,277.238,0,1,5,70.83000000000001,-4.04,COP(=S)(OC)Oc1ccc(N(=O)=O)c(C)c1,COP(=S)(OC)Oc1ccc([N+](=O)[O-])c(C)c1,ZNOLGFHPUIJIMJ-UHFFFAOYSA-N,ZNOLGFHPUIJIMJ-UHFFFAOYSA-N,-4.04,,-0.0,0,0.0,-4.04,0.0
+275,1-Iodonapthalene,-4.888999999999999,1,254.07,0,2,0,0.0,-4.55,Ic1cccc2ccccc12,Ic1cccc2ccccc12,NHPPIJMARIVBGU-UHFFFAOYSA-N,NHPPIJMARIVBGU-UHFFFAOYSA-N,-4.55,,-0.0,0,0.0,-4.55,0.0
+277,Ethanethiol,-0.968,1,62.137,1,0,0,0.0,-0.6,CCS,CCS,DNJIEGIFACGWOD-UHFFFAOYSA-N,DNJIEGIFACGWOD-UHFFFAOYSA-N,-0.6,,-0.0,0,1.0,-0.6,1.0
+278,"1,1,2-Trichloroethane",-1.961,1,133.405,0,0,1,0.0,-1.48,ClCC(Cl)Cl,ClCC(Cl)Cl,UBOXGVDOUJQMTN-UHFFFAOYSA-N,UBOXGVDOUJQMTN-UHFFFAOYSA-N,-1.48,,-0.0,0,1.0,-1.48,1.0
+279,Pyrolan,-3.141,1,245.282,0,2,2,47.36000000000001,-2.09,CN(C)C(=O)Oc1cc(C)nn1c2ccccc2,Cc1cc(OC(=O)N(C)C)n(-c2ccccc2)n1,GEDIWDLJKRKBFT-UHFFFAOYSA-N,GEDIWDLJKRKBFT-UHFFFAOYSA-N,-2.09,,-0.0,0,1.0,-2.09,1.0
+280,o-Hydroxybenzamide,-1.942,1,137.13799999999998,2,1,1,63.32000000000001,-1.82,NC(=O)c1ccccc1O,NC(=O)c1ccccc1O,SKZKKFZAGNVIMN-UHFFFAOYSA-N,SKZKKFZAGNVIMN-UHFFFAOYSA-N,-1.82,,-0.0,0,1.0,-1.82,1.0
+281,o-Nitrotoluene,-2.589,1,137.138,0,1,1,43.14,-2.33,Cc1ccccc1N(=O)=O,Cc1ccccc1[N+](=O)[O-],PLAZTCDQAHEYBI-UHFFFAOYSA-N,PLAZTCDQAHEYBI-UHFFFAOYSA-N,-2.33,,-0.0,0,1.0,-2.33,1.0
+282,"5,5-Diisopropylbarbital",-1.942,1,212.249,2,1,2,75.27000000000001,-2.766,O=C1NC(=O)NC(=O)C1(C(C)C)C(C)C,CC(C)C1(C(C)C)C(=O)NC(=O)NC1=O,NSQYIEOSFPLTJA-UHFFFAOYSA-N,NSQYIEOSFPLTJA-UHFFFAOYSA-N,-2.766,,-0.0,0,1.0,-2.766,1.0
+283,2-Ethyltoluene,-3.2960000000000003,1,120.19499999999996,0,1,1,0.0,-3.21,CCc1ccccc1C,CCc1ccccc1C,HYFLWBNQFMXCPA-UHFFFAOYSA-N,HYFLWBNQFMXCPA-UHFFFAOYSA-N,-3.21,,-0.0,0,0.0,-3.21,0.0
+284,1-Chloroheptane,-3.003,1,134.65,0,0,5,0.0,-4.0,CCCCCCCCl,CCCCCCCCl,DZMDPHNGKBEVRE-UHFFFAOYSA-N,DZMDPHNGKBEVRE-UHFFFAOYSA-N,-4.0,,-0.0,0,0.0,-4.0,0.0
+285,Barbital,-1.265,1,184.195,2,1,2,75.27000000000001,-2.4,O=C1NC(=O)NC(=O)C1(CC)CC,CCC1(CC)C(=O)NC(=O)NC1=O,FTOAOBMCPZCFFF-UHFFFAOYSA-N,FTOAOBMCPZCFFF-UHFFFAOYSA-N,-2.4,,-0.0,0,1.0,-2.4,1.0
+286,Bibenzyl ,-4.301,2,182.266,0,2,3,0.0,-4.62,C(Cc1ccccc1)c2ccccc2,c1ccc(CCc2ccccc2)cc1,QWUWMCYKGHVNAV-UHFFFAOYSA-N,QWUWMCYKGHVNAV-UHFFFAOYSA-N,-4.62,,-0.0,0,0.0,-4.62,0.0
+287,"1,1,2,2-Tetrachloroethane",-2.549,1,167.85,0,0,1,0.0,-1.74,ClC(Cl)C(Cl)Cl,ClC(Cl)C(Cl)Cl,QPFMBZIOSGYJDE-UHFFFAOYSA-N,QPFMBZIOSGYJDE-UHFFFAOYSA-N,-1.74,,-0.0,0,1.0,-1.74,1.0
+288,RTI 23,-4.228,1,283.331,1,3,2,54.46,-5.153,CCN2c1cc(OC)cc(C)c1NC(=O)c3cccnc23 ,CCN1c2cc(OC)cc(C)c2NC(=O)c2cccnc21,PVVKNINYCJVIFZ-UHFFFAOYSA-N,PVVKNINYCJVIFZ-UHFFFAOYSA-N,-5.153,,-0.0,0,0.0,-5.153,0.0
+289,2-Methylphenanthrene,-4.87,1,192.261,0,3,0,0.0,-5.84,Cc1ccc2c(ccc3ccccc32)c1,Cc1ccc2c(ccc3ccccc32)c1,KANLOADZXMMCQA-UHFFFAOYSA-N,KANLOADZXMMCQA-UHFFFAOYSA-N,-5.84,,-0.0,0,0.0,-5.84,0.0
+290,dibutylphthalate,-4.378,1,278.348,0,1,8,52.60000000000001,-4.4,CCCCOC(=O)c1ccccc1C(=O)OCCCC ,CCCCOC(=O)c1ccccc1C(=O)OCCCC,DOIRQSBPFJWKBE-UHFFFAOYSA-N,DOIRQSBPFJWKBE-UHFFFAOYSA-N,-4.4,,-0.0,0,0.0,-4.4,0.0
+291,tetrachloroguaiacol,-4.299,1,261.919,1,1,1,29.46,-4.02,COc1c(O)c(Cl)c(Cl)c(Cl)c1Cl ,COc1c(O)c(Cl)c(Cl)c(Cl)c1Cl,YZZVKLJKDFFSFL-UHFFFAOYSA-N,YZZVKLJKDFFSFL-UHFFFAOYSA-N,-4.02,,-0.0,0,0.0,-4.02,0.0
+292,Dimecron,-2.426,1,299.6909999999999,0,0,8,65.07000000000001,0.523,CCN(CC)C(=O)C(=CCOP(=O)(OC)OC)Cl,CCN(CC)C(=O)C(Cl)=CCOP(=O)(OC)OC,RWXKUYSKUSXRGL-UHFFFAOYSA-N,RWXKUYSKUSXRGL-UHFFFAOYSA-N,0.523,,0.0,0,1.0,0.523,1.0
+293,Equilin,-3.555,1,268.356,1,4,0,37.3,-5.282,CC34CCC1C(=CCc2cc(O)ccc12)C3CCC4=O,CC12CCC3C(=CCc4cc(O)ccc43)C1CCC2=O,WKRLQDKEXYKHJB-UHFFFAOYSA-N,WKRLQDKEXYKHJB-UHFFFAOYSA-N,-5.282,,-0.0,0,0.0,-5.282,0.0
+294,Chlorimuron-ethyl (ph 7),-3.719,1,414.82700000000017,1,2,8,127.79,-4.5760000000000005,CCOC(=O)c1ccccc1S(=O)(=O)NN(C=O)c2nc(Cl)cc(OC)n2,CCOC(=O)c1ccccc1S(=O)(=O)NN(C=O)c1nc(Cl)cc(OC)n1,MBDMKWJEKDHRRP-UHFFFAOYSA-N,MBDMKWJEKDHRRP-UHFFFAOYSA-N,-4.5760000000000005,,-0.0,0,0.0,-4.5760000000000005,0.0
+295,p-Nitroanisole,-2.522,1,153.13699999999997,0,1,2,52.37,-2.41,COc1ccc(cc1)N(=O)=O,COc1ccc([N+](=O)[O-])cc1,BNUHAJGCKIQFGE-UHFFFAOYSA-N,BNUHAJGCKIQFGE-UHFFFAOYSA-N,-2.41,,-0.0,0,1.0,-2.41,1.0
+296,1-Chlorohexane,-2.648,1,120.623,0,0,4,0.0,-3.12,CCCCCCCl,CCCCCCCl,MLRVZFYXUZQSRU-UHFFFAOYSA-N,MLRVZFYXUZQSRU-UHFFFAOYSA-N,-3.12,,-0.0,0,0.0,-3.12,0.0
+297,"2,2',3,3',4,4',5,5'-PCB",-8.468,1,429.77200000000016,0,2,1,0.0,-9.16,Clc1cc(c(Cl)c(Cl)c1Cl)c2cc(Cl)c(Cl)c(Cl)c2Cl,Clc1cc(-c2cc(Cl)c(Cl)c(Cl)c2Cl)c(Cl)c(Cl)c1Cl,DTMRKGRREZAYAP-UHFFFAOYSA-N,DTMRKGRREZAYAP-UHFFFAOYSA-N,-9.16,,-0.0,0,0.0,-9.16,0.0
+298,Raffinose,0.496,1,504.4380000000001,11,3,8,268.67999999999995,-0.41,OCC1OC(CO)(OC2OC(COC3OC(CO)C(O)C(O)C3O)C(O)C(O)C2O)C(O)C1O,OCC1OC(OCC2OC(OC3(CO)OC(CO)C(O)C3O)C(O)C(O)C2O)C(O)C(O)C1O,MUPFEKGTMRGPLJ-UHFFFAOYSA-N,MUPFEKGTMRGPLJ-UHFFFAOYSA-N,-0.41,,-0.0,0,1.0,-0.41,1.0
+299,hexacosane,-9.702,1,366.7180000000002,0,0,23,0.0,-8.334,CCCCCCCCCCCCCCCCCCCCCCCCCC,CCCCCCCCCCCCCCCCCCCCCCCCCC,HMSWAIKSFDFLKN-UHFFFAOYSA-N,HMSWAIKSFDFLKN-UHFFFAOYSA-N,-8.334,,-0.0,0,0.0,-8.334,0.0
+300,RTI 5,-3.471,1,253.30499999999995,0,3,1,36.44,-3.324,CCN2c1ccccc1N(C)C(=O)c3cccnc23 ,CCN1c2ccccc2N(C)C(=O)c2cccnc21,QIBWCUCLHZVYNL-UHFFFAOYSA-N,QIBWCUCLHZVYNL-UHFFFAOYSA-N,-3.324,,-0.0,0,0.0,-3.324,0.0
+301,"1,1-Dichloroethane",-1.5759999999999998,1,98.96,0,0,0,0.0,-1.29,CC(Cl)Cl,CC(Cl)Cl,SCYULBFZEHDVBN-UHFFFAOYSA-N,SCYULBFZEHDVBN-UHFFFAOYSA-N,-1.29,,-0.0,0,1.0,-1.29,1.0
+302,Sulfanilamide,-0.954,1,172.20899999999995,2,1,1,86.18,-1.34,Nc1ccc(cc1)S(N)(=O)=O,Nc1ccc(S(N)(=O)=O)cc1,FDDDEECHVMSUSB-UHFFFAOYSA-N,FDDDEECHVMSUSB-UHFFFAOYSA-N,-1.34,,-0.0,0,1.0,-1.34,1.0
+303,Isopropalin,-5.306,1,309.36600000000004,0,1,8,89.51999999999998,-6.49,CCCN(CCC)c1c(cc(cc1N(=O)=O)C(C)C)N(=O)=O,CCCN(CCC)c1c([N+](=O)[O-])cc(C(C)C)cc1[N+](=O)[O-],NEKOXWSIMFDGMA-UHFFFAOYSA-N,NEKOXWSIMFDGMA-UHFFFAOYSA-N,-6.49,,-0.0,0,0.0,-6.49,0.0
+304,Lindane,-4.009,1,290.832,0,1,0,0.0,-4.64,ClC1C(Cl)C(Cl)C(Cl)C(Cl)C1Cl,ClC1C(Cl)C(Cl)C(Cl)C(Cl)C1Cl,JLYXXMFPNIAWKQ-UHFFFAOYSA-N,JLYXXMFPNIAWKQ-UHFFFAOYSA-N,-4.64,,-0.0,0,0.0,-4.64,0.0
+305,Isofenphos,-4.538,1,345.4010000000002,1,1,8,56.790000000000006,-4.194,CCOP(=S)(NC(C)C)Oc1ccccc1C(=O)OC(C)C,CCOP(=S)(NC(C)C)Oc1ccccc1C(=O)OC(C)C,HOQADATXFBOEGG-UHFFFAOYSA-N,HOQADATXFBOEGG-UHFFFAOYSA-N,-4.194,,-0.0,0,0.0,-4.194,0.0
+306,"1,2,3-Trichlorobenzene",-4.008,1,181.449,0,1,0,0.0,-4.0,Clc1cccc(Cl)c1Cl,Clc1cccc(Cl)c1Cl,RELMFMZEBKVZJC-UHFFFAOYSA-N,RELMFMZEBKVZJC-UHFFFAOYSA-N,-4.0,,-0.0,0,0.0,-4.0,0.0
+307,Tetrachloromethane,-2.607,1,153.823,0,0,0,0.0,-2.31,ClC(Cl)(Cl)Cl,ClC(Cl)(Cl)Cl,VZGDMQKNWNREIO-UHFFFAOYSA-N,VZGDMQKNWNREIO-UHFFFAOYSA-N,-2.31,,-0.0,0,1.0,-2.31,1.0
+308,"3,4-Dichloronitrobenzene",-3.448,1,192.001,0,1,1,43.14,-3.2,O=N(=O)c1cc(Cl)c(Cl)cc1,O=[N+]([O-])c1ccc(Cl)c(Cl)c1,NTBYINQTYWZXLH-UHFFFAOYSA-N,NTBYINQTYWZXLH-UHFFFAOYSA-N,-3.2,,-0.0,0,0.0,-3.2,0.0
+309,Cyclooctanol,-2.14,1,128.215,1,1,0,20.23,-1.29,OC1CCCCCCC1,OC1CCCCCCC1,FHADSMKORVFYOS-UHFFFAOYSA-N,FHADSMKORVFYOS-UHFFFAOYSA-N,-1.29,,-0.0,0,1.0,-1.29,1.0
+310,17a-Methyltestosterone,-4.073,1,302.4580000000001,1,4,0,37.3,-3.999,CC1(O)CCC2C3CCC4=CC(=O)CCC4(C)C3CCC21C,CC12CCC(=O)C=C1CCC1C2CCC2(C)C1CCC2(C)O,GCKMFJBGXUYNAG-UHFFFAOYSA-N,GCKMFJBGXUYNAG-UHFFFAOYSA-N,-3.999,,-0.0,0,0.0,-3.999,0.0
+311,Dulcin,-2.167,1,180.207,2,1,3,64.35,-2.17,CCOc1ccc(NC(N)=O)cc1,CCOc1ccc(NC(N)=O)cc1,GGLIEWRLXDLBBF-UHFFFAOYSA-N,GGLIEWRLXDLBBF-UHFFFAOYSA-N,-2.17,,-0.0,0,1.0,-2.17,1.0
+312,"trans-1,4-Dimethylcyclohexane",-3.305,1,112.216,0,1,0,0.0,-4.47,C/C1CCC(\C)CC1,CC1CCC(C)CC1,QRMPKOFEUHIBNM-UHFFFAOYSA-N,QRMPKOFEUHIBNM-UHFFFAOYSA-N,-4.47,,-0.0,0,0.0,-4.47,0.0
+313,"1,7-phenantroline",-2.994,2,180.21,0,3,0,25.78,-2.68,c1cnc2c(c1)ccc3ncccc23,c1cnc2c(c1)ccc1ncccc12,OZKOMUDCMCEDTM-UHFFFAOYSA-N,OZKOMUDCMCEDTM-UHFFFAOYSA-N,-2.68,,-0.0,0,1.0,-2.68,1.0
+314,Methyl t-butyl ether ,-0.984,1,88.14999999999999,0,0,0,9.23,-0.24,COC(C)(C)C,COC(C)(C)C,BZLVMXJERCGZMT-UHFFFAOYSA-N,BZLVMXJERCGZMT-UHFFFAOYSA-N,-0.24,,-0.0,0,1.0,-0.24,1.0
+315,Anethole,-3.254,1,148.20499999999998,0,1,2,9.23,-3.13,COc1ccc(C=CC)cc1,CC=Cc1ccc(OC)cc1,RUVINXPYWBROJD-UHFFFAOYSA-N,RUVINXPYWBROJD-UHFFFAOYSA-N,-3.13,,-0.0,0,0.0,-3.13,0.0
+316,1-Hexadecanol,-4.94,1,242.44699999999992,1,0,14,20.23,-7.0,CCCCCCCCCCCCCCCCO,CCCCCCCCCCCCCCCCO,BXWNKGSJHAJOGX-UHFFFAOYSA-N,BXWNKGSJHAJOGX-UHFFFAOYSA-N,-7.0,,-0.0,0,0.0,-7.0,0.0
+317,uracil,-0.441,1,112.088,2,1,0,65.72,-1.4880000000000002,O=c1cc[nH]c(=O)[nH]1 ,O=c1cc[nH]c(=O)[nH]1,ISAKRJDGNUQOIC-UHFFFAOYSA-N,ISAKRJDGNUQOIC-UHFFFAOYSA-N,-1.4880000000000002,,-0.0,0,1.0,-1.4880000000000002,1.0
diff --git a/atomsci/ddm/test/integrative/import_transformer/data/scaled_descriptors/H1_std_short_with_rdkit_scaled_descriptors.csv b/atomsci/ddm/test/integrative/import_transformer/data/scaled_descriptors/H1_std_short_with_rdkit_scaled_descriptors.csv
new file mode 100644
index 00000000..606cc9df
--- /dev/null
+++ b/atomsci/ddm/test/integrative/import_transformer/data/scaled_descriptors/H1_std_short_with_rdkit_scaled_descriptors.csv
@@ -0,0 +1,241 @@
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diff --git a/atomsci/ddm/test/integrative/import_transformer/data/scaled_descriptors/aurka_chembl_base_smiles_union_short_with_rdkit_scaled_descriptors.csv b/atomsci/ddm/test/integrative/import_transformer/data/scaled_descriptors/aurka_chembl_base_smiles_union_short_with_rdkit_scaled_descriptors.csv
new file mode 100644
index 00000000..9e8abf60
--- /dev/null
+++ b/atomsci/ddm/test/integrative/import_transformer/data/scaled_descriptors/aurka_chembl_base_smiles_union_short_with_rdkit_scaled_descriptors.csv
@@ -0,0 +1,201 @@
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diff --git a/atomsci/ddm/test/integrative/import_transformer/data/scaled_descriptors/delaney-processed_curated_fit_short_with_rdkit_scaled_descriptors.csv b/atomsci/ddm/test/integrative/import_transformer/data/scaled_descriptors/delaney-processed_curated_fit_short_with_rdkit_scaled_descriptors.csv
new file mode 100644
index 00000000..d056ae71
--- /dev/null
+++ b/atomsci/ddm/test/integrative/import_transformer/data/scaled_descriptors/delaney-processed_curated_fit_short_with_rdkit_scaled_descriptors.csv
@@ -0,0 +1,201 @@
+Compound ID,smiles,dummy_response,MaxEStateIndex_per_heavyatom,MinEStateIndex_per_heavyatom,MaxAbsEStateIndex_per_heavyatom,MinAbsEStateIndex,qed_per_heavyatom,MolWt_per_heavyatom,HeavyAtomMolWt_per_heavyatom,ExactMolWt_per_heavyatom,NumValenceElectrons,NumRadicalElectrons,MaxPartialCharge,MinPartialCharge,MaxAbsPartialCharge,MinAbsPartialCharge,FpDensityMorgan1_per_heavyatom,FpDensityMorgan2_per_heavyatom,FpDensityMorgan3_per_heavyatom,BalabanJ,BertzCT_per_heavyatom,Chi0_per_heavyatom,Chi0n_per_heavyatom,Chi0v_per_heavyatom,Chi1_per_heavyatom,Chi1n_per_heavyatom,Chi1v_per_heavyatom,Chi2n_per_heavyatom,Chi2v_per_heavyatom,Chi3n_per_heavyatom,Chi3v_per_heavyatom,Chi4n_per_heavyatom,Chi4v_per_heavyatom,HallKierAlpha_per_heavyatom,AvgIpc,Kappa1_per_heavyatom,Kappa2_per_heavyatom,Kappa3,LabuteASA_per_heavyatom,PEOE_VSA1_per_heavyatom,PEOE_VSA10_per_heavyatom,PEOE_VSA11_per_heavyatom,PEOE_VSA12_per_heavyatom,PEOE_VSA13,PEOE_VSA14,PEOE_VSA2_per_heavyatom,PEOE_VSA3,PEOE_VSA4,PEOE_VSA5,PEOE_VSA6_per_heavyatom,PEOE_VSA7_per_heavyatom,PEOE_VSA8_per_heavyatom,PEOE_VSA9_per_heavyatom,SMR_VSA1_per_heavyatom,SMR_VSA10_per_heavyatom,SMR_VSA2,SMR_VSA3_per_heavyatom,SMR_VSA4,SMR_VSA5_per_heavyatom,SMR_VSA6,SMR_VSA7,SMR_VSA8,SMR_VSA9,SlogP_VSA1_per_heavyatom,SlogP_VSA10,SlogP_VSA11,SlogP_VSA12,SlogP_VSA2_per_heavyatom,SlogP_VSA3_per_heavyatom,SlogP_VSA4,SlogP_VSA5_per_heavyatom,SlogP_VSA6,SlogP_VSA7,SlogP_VSA8,SlogP_VSA9,TPSA_per_heavyatom,EState_VSA1_per_heavyatom,EState_VSA10_per_heavyatom,EState_VSA11_per_heavyatom,EState_VSA2,EState_VSA3,EState_VSA4,EState_VSA5,EState_VSA6,EState_VSA7,EState_VSA8,EState_VSA9_per_heavyatom,VSA_EState1_per_heavyatom,VSA_EState10_per_heavyatom,VSA_EState2_per_heavyatom,VSA_EState3_per_heavyatom,VSA_EState4_per_heavyatom,VSA_EState5_per_heavyatom,VSA_EState6_per_heavyatom,VSA_EState7_per_heavyatom,VSA_EState8_per_heavyatom,VSA_EState9_per_heavyatom,FractionCSP3,HeavyAtomCount,NHOHCount,NOCount,NumAliphaticCarbocycles,NumAliphaticHeterocycles,NumAliphaticRings,NumAromaticCarbocycles,NumAromaticHeterocycles,NumAromaticRings,NumHAcceptors,NumHDonors,NumHeteroatoms,NumRotatableBonds,NumSaturatedCarbocycles,NumSaturatedHeterocycles,NumSaturatedRings,RingCount,MolLogP,MolMR_per_heavyatom,fr_Al_COO,fr_Al_OH,fr_Al_OH_noTert,fr_ArN,fr_Ar_COO,fr_Ar_N,fr_Ar_NH,fr_Ar_OH,fr_COO,fr_COO2,fr_C_O,fr_C_O_noCOO,fr_C_S,fr_HOCCN,fr_Imine,fr_NH0,fr_NH1,fr_NH2,fr_N_O,fr_Ndealkylation1,fr_Ndealkylation2,fr_Nhpyrrole,fr_SH,fr_aldehyde,fr_alkyl_carbamate,fr_alkyl_halide,fr_allylic_oxid,fr_amide,fr_amidine,fr_aniline,fr_aryl_methyl,fr_azide,fr_azo,fr_barbitur,fr_benzene,fr_benzodiazepine,fr_bicyclic,fr_diazo,fr_dihydropyridine,fr_epoxide,fr_ester,fr_ether,fr_furan,fr_guanido,fr_halogen,fr_hdrzine,fr_hdrzone,fr_imidazole,fr_imide,fr_isocyan,fr_isothiocyan,fr_ketone,fr_ketone_Topliss,fr_lactam,fr_lactone,fr_methoxy,fr_morpholine,fr_nitrile,fr_nitro,fr_nitro_arom,fr_nitro_arom_nonortho,fr_nitroso,fr_oxazole,fr_oxime,fr_para_hydroxylation,fr_phenol,fr_phenol_noOrthoHbond,fr_phos_acid,fr_phos_ester,fr_piperdine,fr_piperzine,fr_priamide,fr_prisulfonamd,fr_pyridine,fr_quatN,fr_sulfide,fr_sulfonamd,fr_sulfone,fr_term_acetylene,fr_tetrazole,fr_thiazole,fr_thiocyan,fr_thiophene,fr_unbrch_alkane,fr_urea
+Carbophenthion,CCOP(=S)(OCC)SCSc1ccc(Cl)cc1,0,0.454454758755354,-0.26863254288054955,0.454454758755354,0.0276363693625592,0.019744837117602356,19.0486111111111,18.152611111111113,18.99854761455558,102,0,0.2476397515720412,-0.32194896053617705,0.32194896053617705,0.2476397515720412,0.07716049382716049,0.1111111111111111,0.1419753086419753,3.518993551043863,51.0786626601621,1.5162515966602195,1.3536899733793613,0.6925703000350028,0.8205485205869394,0.6633277614446941,0.510782931505912,0.12520560039835094,0.5265714140306619,0.06551372329546652,0.3426797903586053,0.033914269055140674,0.26341297049975404,0.04333333333333335,2.577656267044616,0.27266377348437226,0.4918350375435543,6.286951762872694,8.30855893883306,0.5026385735235352,0.0,0.0,0.31630764448766974,0.0,0.0,0.0,0.0,0.0,11.761884949391115,1.276814704796982,2.760017508549066,1.0079260070155789,1.7701413842503173,1.7210906234526193,2.9024968513974168,0.0,0.0,0.0,1.0332926505616185,18.154959354803516,29.192308144394506,0.0,0.0,0.0,0.0,0.0,40.438087236514846,1.0086088530446398,1.158575022892349,0.0,1.979773396295271,29.065158306170954,5.022633313741326,0.0,0.0,1.0255555555555556,3.9800040450047334,0.0,0.0,23.143609745504275,0.0,0.0,0.0,0.0,0.0,0.0,3.0215237333899094,7.27482585655303,0.5787013953947702,-0.031064136817278314,-0.18956677247380652,-0.26863254288054955,0.0,-0.15724010489678067,0.0,-0.7903570282127184,0.0,0.45454545454545453,18,0,2,0,0,0,1,0,1,5,0,7,10,0,0,0,1,5.420300000000004,4.850222222222224,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0
+Perylene,c1cc2cccc3c4cccc5cccc(c(c1)c23)c54,0,0.4259781273620559,-0.027969130553455967,0.4259781273620559,0.021239606953892265,0.012172709186238084,12.615800000000004,12.010999999999997,12.6046950192,92,0,0.06294722286756059,-0.06101065729867257,0.06294722286756059,0.06101065729867257,0.0225,0.0375,0.052500000000000005,2.4408230397617183,72.21145955831491,1.177350269189626,1.1,0.5,0.7464101615137755,0.6,0.3,0.225,0.225,0.175,0.175,0.134375,0.134375,-0.12999999999999995,2.7593809320513794,0.20975581770590562,0.1743260783821579,1.1198586037116531,6.6498366064293535,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.6254512245979766,2.154489685785918,0.0,0.822455133702132,0.822455133702132,2.154489685785918,0.0,0.0,0.0,0.0,0.0,72.50902449195954,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.822455133702132,72.50902449195954,0.0,43.089793715718365,0.0,0.0,3.6254512245979766,0.0,0.0,43.089793715718365,0.0,0.0,0.0,0.0,0.0,0.0,0.822455133702132,5.013934555303604,0.0,0.0,-0.038825585789871386,0.0,0.0,-0.30844230284706464,0.0,0.0,0.0,0.0,20,0,0,0,0,0,5,0,5,0,0,0,0,0,0,0,5,5.737200000000003,4.382600000000002,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+Methanol,CO,0,3.0520833333333335,-1.2708333333333333,3.0520833333333335,2.5416666666666665,0.19264220223692158,16.020999999999997,14.004999999999999,16.013107374,14,0,0.20962966438668965,-0.3996302435573072,0.3996302435573072,0.20962966438668965,1.75,2.0,2.0,3.1684898726705697,18.65148445440323,2.603553390593274,2.4541241452319316,0.45412414523193156,1.2803300858899107,1.0561862178478973,0.10206207261596577,0.0,0.0,0.0,0.0,0.0,0.0,-0.02,1.3516441151533922,0.03671175858480749,0.4800000000000008,-27.040000000000028,9.620018519412026,2.5544040955536063,0.0,0.0,0.7155998286163171,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.5189762294412943,2.0561378342553303,4.610541929808937,0.0,1.4311996572326342,0.0,0.0,0.0,7.037952458882589,0.0,0.0,0.0,0.0,0.0,0.0,0.0,6.7889801536112175,0.0,0.0,2.0561378342553303,0.0,0.0,0.0,0.0,10.115,3.5189762294412943,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.1088081911072125,2.7717376628716472,12.052083333333334,0.0,0.0,1.59375,0.0,0.0,0.0,0.0,0.0,-1.2708333333333333,1.0,2,1,1,0,0,0,0,0,0,1,1,1,1,0,0,0,0,-0.3915,4.0714,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+d-Limonene,CC1=CCC(CC1)C(C)=C,0,0.8264120370370371,-0.3838425925925928,0.8264120370370371,1.523816609977324,0.048503449155951596,13.623800000000003,12.010999999999997,13.612520051199999,56,0,0.057545605380062356,-0.099849370815529,0.099849370815529,0.057545605380062356,0.22999999999999998,0.32999999999999996,0.4,4.183901939448396,65.34983971489392,2.1309401076758503,2.1,0.5,1.1092093147209607,1.05,0.25,0.1625,0.1625,0.0875,0.0875,0.04375,0.04375,-0.052000000000000005,2.3147519262307004,0.10500287602059302,0.30462804076265615,1.7285097165599768,8.611822084218621,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.372931976817597,3.8716516623942576,1.781986123021286,0.41122756685106604,2.1932136898723518,0.0,0.0,0.0,5.893957685363079,3.2822558938579496,0.0,23.72931976817597,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.893957685363079,5.475469583730307,23.72931976817597,0.0,0.0,0.0,0.0,6.244583639211853,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.1932136898723518,12.24399352796674,0.0,0.0,0.0,-0.31782454648526065,-0.37644675925925924,-0.16012731481481476,-1.1132523148148152,-0.8596759259259258,0.0,0.6,10,0,0,1,0,1,0,0,0,0,0,0,3,0,0,0,1,3.308900000000002,4.591200000000002,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+Fenitrothion,COP(=S)(OC)Oc1ccc(N(=O)=O)c(C)c1,0,0.6577537757591048,-0.28456078264639195,0.6577537757591048,1.205038580246913,0.02752672958898329,16.308117647058808,15.596588235294114,16.295140006705903,94,0,0.3795196463794277,-0.4241224514170116,0.4241224514170116,0.3795196463794277,0.08996539792387542,0.13148788927335642,0.16262975778546712,3.7835839547429773,49.736663032428474,1.362516013405119,1.1672892313990055,0.5620494532771021,0.7461632386650888,0.5694955499091827,0.36690958530275775,0.14152444719526472,0.3052768316073209,0.08111992037094129,0.17077962462593704,0.04310161988511817,0.08135480149055785,-0.05941176470588235,2.4073276537259276,0.26977208663356883,0.32652030832053186,3.472275419065506,7.103494998760817,0.7983083226550266,0.338206578428465,0.0,0.0,5.687386274683562,6.718607232363179,0.5949598981626807,0.0,0.0,0.0,0.0,1.1139252666248394,2.4471121690749875,1.0153022384911559,2.055509129882104,1.4242852703344346,0.0,0.0,0.0,0.4030524774879811,14.075904917765177,33.80502588345245,0.0,5.749511833283905,0.2661027742183422,5.687386274683562,5.749511833283905,6.718607232363179,1.1176009392107558,1.226726494827193,16.966210386061253,1.294856127396449,18.127256122989884,0.0,0.0,0.0,4.166470588235295,3.9821953552291793,0.5949598981626807,0.0,0.0,0.0,0.0,0.0,0.0,0.0,20.85435041206228,1.2336970491620267,5.969453967039833,0.27563427531506435,0.5824651881018197,0.6577537757591048,-0.156211952373542,-0.07088462236746547,-0.22310132386287845,0.0,-0.4798557524861798,-0.39512283617150595,0.3333333333333333,17,0,6,0,0,0,1,0,1,6,0,8,8,0,0,0,1,2.7993200000000007,3.9273176470588242,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+Dialifor,CCOP(=S)(OCC)SC(CCl)N1C(=O)c2ccccc2C1=O,0,0.577347996565776,-0.2331650309524418,0.577347996565776,0.32551256613756685,0.016492638809130253,17.124086956521737,16.37904347826088,17.087065840608712,126,0,0.2620782456005316,-0.3219183835718298,0.3219183835718298,0.2620782456005316,0.06049149338374291,0.0831758034026465,0.10207939508506617,2.8361856967751575,53.037992664650616,1.383368381142012,1.205299191599666,0.6089230741593976,0.7590187142011106,0.60264000431541,0.42601811214907076,0.15306774032987813,0.4201660723986393,0.1008044188536547,0.2902351089344405,0.06554482049881696,0.21834193987202677,-0.018695652173913047,2.9701569062056223,0.27739204216478497,0.35211798665655225,3.9780820758722486,7.522078018858662,0.3933693184097233,0.23260541096393153,0.0,0.24754511307730673,11.814359458703011,0.0,0.6299558303910487,0.0,0.0,11.600939890232516,0.5254277137098518,2.1294435884639027,0.47678558475485916,1.8440785738318382,1.8234549628896102,2.2738007754115386,0.0,0.21303955351523818,0.0,0.8284221168157297,18.950077209206285,35.29657781404717,0.0,0.0,0.0,0.0,0.0,28.67620228712373,1.783229167431748,0.9067108874809687,0.0,2.509680740914034,24.16967483065318,0.0,0.0,0.0,2.427826086956522,4.443042850678674,0.4169162768758106,0.0,4.899909730850478,0.0,0.0,0.0,0.0,0.0,0.0,2.4242689459647213,6.102232081106265,0.4349731322793923,1.1405432728647011,-0.16139691987906277,-0.3201277625023887,-0.3081485247077898,-0.17623725905313117,0.0,-0.6574901940210307,0.0,0.42857142857142855,23,0,5,0,1,1,1,0,1,6,0,9,10,0,0,0,2,3.878200000000003,4.199043478260871,0,0,0,0,0,0,0,0,0,0,2,2,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+"7,12-Dimethylbenz(a)anthracene",Cc1c2ccccc2c(C)c3ccc4ccccc4c13,0,0.43065547052154196,-0.15896001629818599,0.43065547052154196,0.4993722180230111,0.013843259711468395,12.817400000000003,12.010999999999997,12.8062600256,96,0,0.06295681735762937,-0.06162899004742313,0.06295681735762937,0.06162899004742313,0.0325,0.065,0.1025,2.698207629461001,82.09615720255705,1.3696152422706633,1.3,0.5,0.8175426480542942,0.6875,0.2875,0.2125,0.2125,0.159375,0.159375,0.1140625,0.1140625,-0.11699999999999995,2.7711713161995633,0.1823584014233803,0.20380118073865203,1.4213973098015722,7.034290447309037,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0212093538316473,2.8574016252387064,0.41122756685106604,0.68537927808511,1.0966068449361759,1.6158672643394385,0.0,0.0,0.0,0.6851892117295679,0.0,71.55109006002695,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,13.703784234591359,1.6529519941058755,60.42418707663295,0.0,32.31734528678877,0.0,0.0,5.878610979070354,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0966068449361759,6.596031579749732,0.0,0.0,-0.17617434859746361,-0.07864158163265304,0.0,-0.39907385530192835,0.0,-0.3171417942176872,0.0,0.1,20,0,0,0,0,0,4,0,4,0,0,0,2,0,0,0,4,5.763040000000005,4.421700000000003,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,4,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+1-Hexyne ,CCCCC#C,0,1.2102430555555557,-0.5682291666666667,1.2102430555555557,1.3722859977324262,0.05863406361705498,13.69099999999999,12.010999999999997,13.679708386666666,34,0,0.12382475651592365,-0.12010931716807507,0.12382475651592365,0.12010931716807507,0.3888888888888889,0.5555555555555556,0.6666666666666666,5.583773441038952,52.071086402273345,2.235702260395516,2.1666666666666665,0.5,1.1351100286299702,1.0416666666666667,0.20833333333333334,0.08333333333333333,0.08333333333333333,0.03125,0.03125,0.010416666666666666,0.010416666666666666,-0.07333333333333333,2.0052437236705822,0.09089300809081026,0.7599999999999997,3.5599999999999987,8.935481998891671,0.0,0.22845975936170337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,12.319835853677588,2.2041361697708433,1.0621541502215632,3.1182919844768926,0.0,2.284597593617033,0.0,0.0,0.0,0.0,4.328444470213969,0.0,0.0,0.0,12.319835853677588,0.0,0.0,0.0,0.0,0.0,0.0,12.319835853677588,6.6130420638310055,0.0,0.0,0.0,0.0,0.0,4.328444470213969,0.0,0.0,0.0,0.0,0.0,12.319835853677588,0.0,0.0,0.0,2.284597593617033,11.710371787603933,0.0,0.0,0.0,0.0,0.25422453703703707,0.0,-1.4102902966742255,-0.5543060279667423,0.0,0.6666666666666666,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,1.8098,4.726333333333331,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0
+"1,3-Benzenediol",Oc1cccc(O)c1,0,0.9139467592592593,-0.06759259259259257,0.9139467592592593,0.4923611111111106,0.06557301753637214,13.763999999999994,13.007999999999997,13.754597429,42,0,0.2930955253024167,-0.5078233297968214,0.5078233297968214,0.2930955253024167,0.171875,0.25,0.3125,3.5553379241657663,41.8530926650916,1.3597893971888562,1.2270620726159658,0.4770620726159658,0.8175139025074156,0.5905931089239487,0.2385310363079829,0.1447810363079829,0.1447810363079829,0.07239051815399145,0.07239051815399145,0.03879942574366239,0.03879942574366239,-0.14750000000000002,2.1641757289633556,0.17569698404160816,0.21726506702454224,0.9041418956093399,6.952879267643794,1.2772020477768031,1.4373779583209763,0.0,0.0,2.8623993144652684,0.0,0.0,0.0,0.0,0.0,0.7553023384579118,1.5106046769158237,0.7553023384579118,0.6853792780851101,1.962581325861913,0.0,2.8623993144652684,0.0,0.0,0.0,0.0,24.16967483065318,0.0,11.49902366656781,0.0,0.0,11.49902366656781,0.0,1.6350019620849616,0.0,0.0,0.6853792780851101,24.16967483065318,0.0,0.0,0.0,5.0575,4.458587312152623,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,10.217616382214425,1.0431791923932687,5.275069444444444,0.0,0.0,0.9906018518518518,0.0,-0.1230902777777777,-0.26758101851851834,0.0,0.0,0.0,0.0,8,2,2,0,0,0,1,0,1,2,2,2,2,0,0,0,1,1.0977999999999999,3.721449999999999,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+nevirapine,Cc3ccnc4N(C1CC1)c2ncccc2C(=O)Nc34 ,0,0.6606787131519273,-0.16057300524376422,0.6606787131519273,0.06943263416477707,0.04308580625432095,13.31519999999999,12.609599999999999,13.305838053400015,100,0,0.259040848923194,-0.3184743028957275,0.3184743028957275,0.259040848923194,0.075,0.11750000000000001,0.1575,2.2561509939783906,63.0608931811301,1.2959883530919116,1.1848551336231847,0.4848551336231847,0.7667564324352896,0.6206899460240851,0.27332926624908715,0.1959503473204158,0.1959503473204158,0.13656774513810382,0.13656774513810382,0.0912973639798205,0.0912973639798205,-0.12150000000000001,3.057425241892186,0.20183107045365561,0.2015211227464718,1.5916576426488964,6.796985157041073,0.5104861542069428,0.2908931388917514,0.36148524280816147,0.0,5.907179729351506,0.0,0.2397268592035911,9.967957041894417,0.0,0.0,0.0,2.1644224767320663,1.397949639158535,0.9737694551700938,1.201312024630644,1.161514577985256,0.0,0.4983978520947209,0.0,1.2807817194152042,10.209723084138854,41.5999495280134,0.0,0.0,0.5104861542069428,17.323111830353618,0.0,0.0,1.0946514619797783,0.0,6.851892117295679,2.3243575600168733,30.473046544619407,1.4118420783282006,0.0,0.0,2.906,4.52229377380113,0.2397268592035911,0.0,5.309813353288376,4.899909730850478,0.0,0.0,0.0,0.0,9.967957041894417,0.9615851654270529,5.620782621960821,0.0,1.0485260298563868,-0.0034716317082388536,-0.14314734504913074,-0.1615078184051398,-0.29013975169543127,-0.39380243304883694,-0.16057300524376422,0.0,0.26666666666666666,20,1,5,1,1,2,0,2,2,4,1,5,2,1,0,1,4,2.6512200000000004,3.816260000000001,0,0,0,0,0,2,0,0,0,0,1,1,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0
+Diisopropyl ether ,CC(C)OC(C)C,0,1.0674107142857143,-0.5549107142857146,1.0674107142857143,3.7072916666666664,0.0737773043203177,14.596714285714276,12.580714285714285,14.586352152571427,44,0,0.05975191070812216,-0.37614367594581366,0.37614367594581366,0.05975191070812216,0.1836734693877551,0.24489795918367346,0.2857142857142857,6.154787845104639,43.269481572065025,2.5295866830266496,2.486892612923409,0.486892612923409,1.243872397312364,1.2011783272091232,0.2011783272091233,0.10861576615426127,0.10861576615426127,0.029160592175990218,0.029160592175990218,0.014580296087995109,0.014580296087995109,-0.005714285714285714,2.0920500810351244,0.08865079715686741,0.4324073759507766,5.960000000000001,9.335299118809841,0.6766947076857213,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.9153669241689597,2.3498718105775196,2.1287933094629166,3.418211820026161,0.0,0.0,0.0,0.0,5.652514931868955,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.7371480076999966,0.6766947076857213,0.0,6.656884036509404,0.0,0.0,0.0,0.0,1.3185714285714287,5.652514931868955,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.736862953800049,2.7415171123404396,14.799702380952384,0.0,0.0,0.0,0.0,0.0,0.0,-1.1098214285714287,-2.118452380952381,0.0,1.0,7,0,1,0,0,0,0,0,0,1,0,1,6,0,0,0,0,1.8197999999999999,4.479571428571425,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
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+4-Methyl-2-pentanone,CC(C)CC(=O)C,0,1.6384027777777777,-0.5562996031746031,1.6384027777777777,2.220138888888888,0.07357418815616426,14.308714285714276,12.580714285714283,14.298402143428572,42,0,0.12959512117371577,-0.3000199627172533,0.3000199627172533,0.12959512117371577,0.30612244897959184,0.40816326530612246,0.4693877551020408,6.146307928925497,48.840938976187566,2.2967643241699465,2.2011783272091234,0.486892612923409,1.1292429340541787,1.064874877890276,0.20773202074741878,0.11844630646170451,0.11844630646170451,0.03407586232971184,0.03407586232971184,0.016218719472568983,0.016218719472568983,-0.047142857142857146,2.092914074948499,0.09811170266429532,0.3970700331992924,5.669999999999999,8.797875671695735,0.6849338834388317,0.8261778494807056,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.9576834620844799,1.8208356860941082,3.260289653624574,0.0,3.0348056940163515,0.8261778494807056,0.0,0.0,5.893957685363079,3.8469430361737738,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.8261778494807056,0.6849338834388317,5.893957685363079,6.196814846751297,0.0,0.0,0.0,0.0,2.4385714285714286,5.515114840706348,0.6849338834388317,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.3498718105775196,12.061008597883598,0.0,1.6384027777777777,0.0,0.0,-0.8709490740740742,0.0,-0.5562996031746031,-1.5697817460317463,0.0,0.8333333333333334,7,0,1,0,0,0,0,0,0,1,0,1,5,0,0,0,0,1.6215,4.3051428571428545,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+4-Chlorophenol ,Oc1ccc(Cl)cc1,0,0.9066724537037036,-0.055439814814814775,0.9066724537037036,0.26777777777777745,0.07088846935439169,16.069749999999996,15.439749999999998,16.0003615575,42,0,0.2930920867052697,-0.5079651858460357,0.5079651858460357,0.2930920867052697,0.1875,0.265625,0.328125,3.5637576760479117,40.54540340653495,1.271401049540538,1.0982765954341362,0.5677677136864431,0.7502633021998175,0.537669334025051,0.28388385684322154,0.14288829771706812,0.19013385684322154,0.07144414885853406,0.09506692842161077,0.03572207442926703,0.047533464210805385,-0.08625000000000001,2.1268719300323427,0.24008775054260995,0.2565836934864397,1.138019488647966,7.45505323219262,0.6386010238884016,0.7186889791604881,0.0,0.0,1.4311996572326342,0.0,0.0,0.0,0.0,0.0,1.4501174862790644,3.0212093538316473,0.6278291642176658,0.6853792780851101,1.3239803019735117,1.4501174862790644,1.4311996572326342,0.0,0.0,0.0,0.0,29.192308144394506,0.0,5.749511833283905,0.0,0.0,5.749511833283905,11.600939890232516,0.8175009810424808,0.0,0.0,0.6853792780851101,24.16967483065318,5.022633313741326,0.0,0.0,2.52875,3.7398983329921354,0.0,0.0,5.022633313741326,0.0,0.0,0.0,0.0,0.0,5.1088081911072125,2.3143967215182535,4.440681925547997,0.6856722411186695,0.0,0.46262345679012346,0.0,-0.05215277777777774,-0.21043595679012328,0.0,0.0,0.0,0.0,8,1,1,0,0,0,1,0,1,1,1,2,1,0,0,0,1,2.0456,4.1396,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+Butamben,CCCCOC(=O)c1ccc(N)cc1,0,0.8789204392074288,-0.27990523937749706,0.8789204392074288,0.23747088912470415,0.03235152485893754,13.803285714285723,12.723285714285712,13.793591337142857,76,0,0.3376198178852672,-0.46209728634871794,0.46209728634871794,0.3376198178852672,0.12244897959183673,0.17857142857142858,0.22448979591836735,3.4898090483142257,56.17544886140068,1.6661363524788253,1.554550726887692,0.48312215545912035,0.9085657308607991,0.7664576017461207,0.23828423096041243,0.14037060153908398,0.14037060153908398,0.07665623712125261,0.07665623712125261,0.039741049725482216,0.039741049725482216,-0.10785714285714285,2.506398108023622,0.155856521938115,0.37639178308014615,3.0883626680736613,7.504410177502657,0.7468985663947373,0.0,0.20169172547545725,0.0,0.0,5.969305287951849,0.0,4.794537184071822,0.0,0.0,0.9446297870446471,2.181614266570183,1.0916211548482215,1.453356290831043,2.155353251767223,0.8326208259025295,0.0,0.0,0.0,1.399838708568174,12.278702218642561,29.733126322350174,0.0,0.0,0.40855121255187665,5.687386274683562,0.0,0.0,0.8948778950620098,0.33834735384286063,0.0,3.4125422732811534,24.16967483065318,2.8236841566564013,0.0,0.0,3.737142857142857,4.824753217704022,0.34246694171941583,0.0,5.719716975726273,0.0,0.0,0.0,0.0,0.0,4.736862953800049,1.474538956204947,8.254465040995846,0.0,0.8789204392074288,0.0,-0.159267412070539,-0.13754774727351263,-0.30422809136882256,-0.5527699914446044,-0.5390960475696067,0.0,0.36363636363636365,14,2,3,0,0,0,1,0,1,3,1,3,6,0,0,0,1,2.2257,4.0032071428571445,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+Raffinose,OCC1OC(CO)(OC2OC(COC3OC(CO)C(O)C(O)C3O)C(O)C(O)C2O)C(O)C1O,0,0.2674829917331947,-0.1597302373945447,0.2674829917331947,3.6518515550258877,0.00430682491710772,14.836411764705874,13.88770588235295,14.828501027764727,200,0,0.22355033369268296,-0.3935665150995826,0.3935665150995826,0.22355033369268296,0.020761245674740487,0.039792387543252594,0.058823529411764705,2.636011957516158,52.95623667140117,1.5386384852642576,1.3979991955124063,0.45682272492417086,0.8662586998679636,0.6772746542639135,0.2363707955844283,0.16854178917713722,0.16854178917713722,0.11021971151770431,0.11021971151770431,0.06388221885991079,0.06388221885991079,-0.018823529411764708,3.419547689765725,0.2264435703530288,0.3287381161698893,5.08976591342448,7.004191169954247,2.349447202093518,2.159975986451945,0.3685928452080528,0.633244334768517,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.4253789825977508,3.1960921926692394,0.0,15.743196229558976,0.0,0.0,2.5058666525321835,26.235940971665155,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.393396867338172,0.6965974932058896,0.0,0.846644990575724,0.0,0.0,0.0,0.0,7.902352941176469,3.2775119752282174,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,65.67061600977945,1.727638669721581,8.383058757324392,0.0,0.0,1.2515960417378058,0.0,-0.15350836138940052,0.0,-1.902575776914317,-0.5638647784055403,0.0,1.0,34,11,16,0,3,3,0,0,0,16,11,16,19,0,3,3,3,-7.571399999999989,2.9786705882352944,0,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+3-Heptanoyloxymethylphenytoin,O=C1N(COC(=O)CCCCCC)C(=O)C(N1)(c2ccccc2)c3ccccc3,0,0.5003707696028359,-0.16283162620406275,0.5003707696028359,0.6797559392394255,0.013685865092524362,13.602448275862058,12.69872413793104,13.592733010758646,152,0,0.32786640076668144,-0.44359585955322295,0.44359585955322295,0.32786640076668144,0.03804994054696789,0.05945303210463733,0.07966706302021404,2.3553179845627294,64.3002238359476,1.5007607364624205,1.3802558742363922,0.483704150098461,0.8388536640441648,0.7012368382899831,0.2547811970658467,0.1672812822919732,0.1672812822919732,0.11284594690165164,0.11284594690165164,0.07380443482547293,0.07380443482547293,-0.10862068965517237,3.2428128472531967,0.1898208528875177,0.32294094787105043,4.312663151098256,7.1376595327739825,0.3464371140375319,0.0,0.47012715022808277,0.0,5.907179729351506,12.000419800289922,0.33065773683253946,9.694446914922299,0.0,0.0,2.9791328930316134,0.60344234085253,0.8342340045359391,0.5672104370359531,1.8896993271930778,0.617503432056601,0.0,0.35205941669444324,0.0,1.5260497198733076,6.682920025902854,71.55109006002695,0.0,0.0,0.18309701218235777,4.794537184071822,0.0,0.0,1.0169113547032675,0.6849952611836908,0.0,2.900427017641105,60.42418707663295,1.4118420783282006,0.0,0.0,2.6106896551724135,5.19333705434607,0.49598660524880916,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.736862953800049,1.230372620089093,7.334314275253731,0.0,1.3980180347657576,-0.02343985997377329,-0.23416209330492602,-0.1824604880158491,-0.5343941357264976,-0.7901600866181618,-0.295301853276835,0.0,0.34782608695652173,29,1,6,0,1,1,2,0,2,4,1,6,10,0,1,1,3,3.9531000000000027,3.7503344827586216,0,0,0,0,0,0,0,0,0,0,3,3,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,2,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1
+"1,2,3,5-Tetrachlorobenzene",Clc1cc(Cl)c(Cl)c(Cl)c1,0,0.7295432098765432,-0.016876543209876506,0.7295432098765432,0.002098765432098082,0.044741511729822034,21.5894,21.3878,21.389106078399998,54,0,0.0779030660373676,-0.08423147288483936,0.08423147288483936,0.0779030660373676,0.1,0.13999999999999999,0.18,3.6035674514745475,23.181286524684438,0.9464101615137755,0.651185789203691,0.7535573676110726,0.5464101615137755,0.3255928946018455,0.3767786838055363,0.1505928946018454,0.3017786838055364,0.08243930444377985,0.21517505618848248,0.04300536650760421,0.12365895666566984,0.03799999999999999,2.0815718239857546,0.595884022643223,0.3185466950118093,1.7010964859316502,8.145440902735926,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.640375956093006,1.208483741532659,0.5022633313741326,1.7809417053564416,0.274151711234044,4.640375956093006,0.0,0.0,0.0,0.0,0.0,32.1753706702919,0.0,0.0,0.0,0.0,0.0,46.40375956093006,0.0,0.0,0.0,0.274151711234044,12.08483741532659,20.090533254965305,0.0,0.0,0.0,0.0,0.0,0.0,32.1753706702919,0.0,0.0,0.0,0.0,0.0,0.0,4.914527667327051,1.4590864197530864,2.233234567901234,0.0,-0.014123456790123213,0.0,0.0,-0.03375308641975301,0.0,0.0,0.0,0.0,10,0,0,0,0,0,1,0,1,0,0,4,0,0,0,0,1,4.3002,4.648200000000001,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+2-Ethyltoluene,CCc1ccccc1C,0,0.8509387860082305,-0.36516492399428924,0.8509387860082305,0.8507251511715792,0.059331007394378465,13.354999999999992,12.010999999999997,13.343766709333332,48,0,0.06257013944568376,-0.061984846716096755,0.06257013944568376,0.061984846716096755,0.1851851851851852,0.2839506172839506,0.3703703703703704,3.9821067934401584,62.64518475446344,1.8849001794597504,1.8333333333333333,0.5,1.0151945939942368,0.9166666666666666,0.25,0.1527777777777778,0.1527777777777778,0.09027777777777778,0.09027777777777778,0.04513888888888889,0.04513888888888889,-0.08666666666666667,2.26891889848091,0.11645051156696064,0.29714858458552007,1.3174550052099938,8.153986162744504,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.4468407719943177,2.7057466668910055,1.2184520499290845,0.6092260249645424,1.8276780748936265,0.0,0.0,0.0,0.0,2.2307454595467484,0.0,35.29657781404717,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.7081027668143755,6.851892117295679,3.8253219749702563,24.16967483065318,0.0,0.0,0.0,0.0,6.152587438885323,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.8276780748936265,9.898077286470144,0.0,0.0,0.0,-0.2280735596707818,0.0,-0.4094941525993112,-0.3587834362139919,-0.7072816935416143,0.0,0.3333333333333333,9,0,0,0,0,0,1,0,1,0,0,0,3,0,0,0,1,2.5574200000000005,4.506333333333335,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+RTI 23,CCN2c1cc(OC)cc(C)c1NC(=O)c3cccnc23 ,0,0.6351462937467753,-0.177249076548609,0.6351462937467753,0.017840136054421585,0.04379534211056481,13.491952380952362,12.675952380952378,13.482479846857164,108,0,0.25899656081476785,-0.4966744376742702,0.4966744376742702,0.25899656081476785,0.07709750566893424,0.12244897959183675,0.16099773242630386,2.7361776392511175,63.38996605081243,1.4046246348108504,1.2932446365441714,0.48372082702036195,0.8070047816905651,0.660706362640267,0.25845809618788806,0.1804658556014878,0.1804658556014878,0.1255653452497157,0.1255653452497157,0.08462285789473906,0.08462285789473906,-0.1219047619047619,2.8109580827129124,0.19052979625629207,0.24223757828058143,2.1051981129706134,7.002587763972118,0.7117421922828049,0.5508273624342349,0.06723057515848574,0.0,5.907179729351506,0.0,0.22831129447961057,4.9839785209472085,0.0,0.0,0.0,1.7806900880608685,1.4132496215312265,1.6639164261575463,1.5654942435677897,1.099991193169222,0.0,0.23733231052129564,0.0,0.65256115402816,23.744535227337387,41.46947302486193,0.0,5.749511833283905,0.7117421922828049,17.192635327202154,5.749511833283905,0.0,1.1631414473093926,0.0,6.851892117295679,2.12883186588024,30.34257004146794,1.4118420783282006,0.0,0.0,2.5933333333333333,4.645590299642352,0.22831129447961057,0.0,10.209723084138854,0.0,0.0,0.0,0.0,0.0,9.720841474747257,1.1116180465262722,6.64338607926937,0.0,0.8047815529430464,-0.011080651837454563,-0.22662617615388309,-0.19090017264254316,-0.22086964797828745,-0.04931549431206934,-0.5165664142641185,-0.1613805035954894,0.25,21,1,5,0,1,1,1,1,2,4,1,5,5,0,0,0,3,3.122320000000001,3.933390476190478,0,0,0,0,0,1,0,0,0,0,1,1,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,1,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0
+Caproaldehyde,CCCCCC=O,0,1.5367689099449302,-0.5527976190476193,1.5367689099449302,2.1085955215419494,0.05548488247399498,14.308714285714276,12.580714285714283,14.298402143428572,42,0,0.11949019725434507,-0.303402908155129,0.303402908155129,0.11949019725434507,0.2857142857142857,0.40816326530612246,0.4897959183673469,5.989640448872497,52.12944612028098,2.2967643241699465,2.2011783272091234,0.486892612923409,1.1347679532820092,1.064874877890276,0.20773202074741878,0.08600886751656654,0.08600886751656654,0.03407586232971184,0.03407586232971184,0.012573645450570206,0.012573645450570206,-0.047142857142857146,2.139733248300212,0.09811170266429532,0.8100000000000002,5.669999999999999,8.797875671695735,0.6849338834388317,1.0904243883573517,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.799677417136348,0.9104178430470542,3.064467002743114,0.0,3.0348056940163515,0.8946017374758916,0.0,0.0,0.0,4.620513103230457,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.8946017374758916,0.6849338834388317,0.0,6.970384913807982,0.0,0.0,0.0,0.0,2.4385714285714286,5.515114840706348,0.6849338834388317,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.3498718105775196,12.146176978727997,0.0,1.5367689099449302,0.0,0.0,0.0,0.0,-2.4693694660403844,-0.511195470251593,0.0,0.8333333333333334,7,0,1,0,0,0,0,0,0,1,0,1,5,0,0,0,0,1.7656,4.315142857142854,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
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+2-Chlorotoluene,Cc1ccccc1Cl,0,0.9242505787037038,-0.33067129629629627,0.9242505787037038,0.44351851851851776,0.06268770825084556,15.823249999999994,14.941249999999998,15.752953488,42,0,0.06385044413527102,-0.08405299355229609,0.08405299355229609,0.06385044413527102,0.203125,0.296875,0.390625,3.6807357198826747,47.471916475878245,1.4955127018922192,1.3597455591261534,0.5792366773784602,0.8344283100678678,0.6798727795630767,0.2896183386892301,0.14862277956307668,0.1958683386892301,0.08021708467230752,0.11565125401692257,0.03715569489076918,0.04896708467230752,-0.061250000000000006,2.094639700632517,0.18896314229450825,0.27317010236609646,1.014610261986002,7.991800620243744,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.7160245016528,2.307220289581996,1.1418636227814984,0.6853792780851101,1.1994137366489426,1.4501174862790644,0.0,0.0,0.0,0.8564865146619599,0.0,34.7557596360915,0.0,0.0,0.0,0.0,0.0,11.600939890232516,0.0,0.0,6.851892117295679,1.8948451731110667,24.16967483065318,5.022633313741326,0.0,0.0,0.0,5.200956469173397,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.649531222928007,6.3305314429012345,0.698695023148148,0.0,-0.05543981481481472,-0.0665316358024691,0.0,-0.2814448302469135,0.0,-0.33067129629629627,0.0,0.14285714285714285,8,0,0,0,0,0,1,0,1,0,0,1,1,0,0,0,1,2.6484200000000007,4.523625,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+"1,2,3-Trichlorobenzene",Clc1cccc(Cl)c1Cl,0,0.8044307270233195,-0.03452674897119339,0.8044307270233195,0.037654320987653866,0.05956690614160024,20.161,19.825,19.992225903999998,48,0,0.07779504102309424,-0.08265556792103128,0.08265556792103128,0.07779504102309424,0.1234567901234568,0.1728395061728395,0.2222222222222222,3.6035674514745475,25.811454471390828,1.0515668461264172,0.7926548243364091,0.7112978063425605,0.6071224016819728,0.39632741216820455,0.35564890317128023,0.1463274121682045,0.27231556983794697,0.0811002140206102,0.20758635634754494,0.038565980026178115,0.0859360353166296,0.00999999999999999,2.0815718239857546,0.45223671238799706,0.30913336463671315,1.5113363246814933,8.061790366738379,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.538359819817872,1.3427597128140656,0.0,2.1311306233038483,0.45691951872340675,3.8669799634108384,0.0,0.0,0.0,0.0,0.0,33.19515606421386,0.0,0.0,0.0,0.0,0.0,34.802819670697545,0.0,0.0,0.0,0.45691951872340675,18.127256122989884,15.067899941223978,0.0,0.0,0.0,0.0,0.0,0.0,33.19515606421386,0.0,0.0,0.0,0.0,0.0,0.0,4.323899482134246,2.4110699588477362,1.8532098765432095,0.0,-0.022153635116597896,0.0,0.0,-0.09397805212620017,0.0,0.0,0.0,0.0,9,0,0,0,0,0,1,0,1,0,0,3,0,0,0,0,1,3.6468000000000007,4.6080000000000005,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
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+1-Bromoheptane,CCCCCCCBr,0,0.9502604166666666,-0.5048958333333335,0.9502604166666666,2.2094005102040817,0.05600566745744153,22.387625000000018,20.497625,22.2544640725,50,0,0.03865474351025575,-0.09280780398759336,0.09280780398759336,0.03865474351025575,0.171875,0.265625,0.34375,6.370990656665334,49.96527438819279,2.4375,2.3597455591261536,0.6829951265154914,1.1875,1.1486227795630768,0.3102475632577457,0.08993638978153835,0.13949878162887286,0.03715569489076918,0.06193689081443643,0.014671597445384588,0.027062195407218215,0.06,2.2407403893236806,0.11735901267714442,0.9349999999999999,6.48,10.233876102795962,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,6.0341919525703185,0.7966156126661724,3.2304464085022615,0.0,2.570172292819162,1.9912429872436685,0.0,0.0,0.0,4.839564577992822,5.282192925464797,0.0,0.0,0.0,0.0,0.0,0.0,15.929943897949348,0.6602741156830996,0.0,0.0,7.40973687081199,0.0,0.0,0.0,0.0,0.0,5.499838693675922,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,15.929943897949348,2.570172292819162,13.827335645195578,0.2761750637755102,0.0,-0.3924073926445577,0.0,0.0,0.0,-2.4402779549319735,-0.45832536139455804,0.0,1.0,8,0,0,0,0,0,0,0,0,0,0,1,6,0,0,0,0,3.351700000000002,5.319125000000001,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
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+"1,1,2-Trichloroethane",ClCC(Cl)Cl,0,1.3214506172839506,-0.4785493827160493,1.3214506172839506,2.2692901234567895,0.09605150969974732,26.681000000000004,26.0762,26.386006627199997,32,0,0.12082872501594434,-0.12377171147587997,0.12377171147587997,0.12082872501594434,0.4,0.44000000000000006,0.44000000000000006,4.020391543176799,13.501955000865388,1.4,1.0267786838055364,0.8803360514166089,0.65,0.46338934190276815,0.39016802570830456,0.07098038523709835,0.2986554414255808,0.014285714285714282,0.1285714285714286,0.0,0.0,0.174,1.253297578463043,0.449548501781652,0.6150677618069815,4.87,10.045780664674925,0.0,0.962464265890348,0.0,0.0,0.0,0.0,0.0,0.0,0.0,34.802819670697545,0.0,0.0,0.548303422468088,1.440573055908786,0.8224551337021321,6.960563934139509,0.0,0.0,0.0,0.962464265890348,5.832106723373709,0.0,0.0,0.0,0.0,0.0,0.0,34.802819670697545,2.1288856105650895,0.0,0.0,0.8224551337021321,0.0,0.0,0.0,0.0,0.0,2.1288856105650895,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,7.783019067841643,3.9470679012345684,2.952006172839506,-0.4538580246913579,0.0,0.0,-0.4785493827160493,0.0,0.0,0.0,0.0,1.0,5,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,2.0289,5.242799999999999,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+2-bromonaphthalene,c1c(Br)ccc2ccccc12 ,0,0.7120265151515152,-0.04148800934515218,0.7120265151515152,0.033449074074073826,0.05623226114894806,18.824545454545458,18.183090909090904,18.72482839309091,54,0,0.06411674422553322,-0.06162969636397052,0.06411674422553322,0.06162969636397052,0.09090909090909091,0.15702479338842976,0.23140495867768596,3.2161246449441507,51.56374976119334,1.2521366083542054,1.1252694975462934,0.6330873647385392,0.7532244381985159,0.5853620215004194,0.3392709550965423,0.17627111240951032,0.24836186418745143,0.11086282893202788,0.14690820482099845,0.06894257624447546,0.09597660816120338,-0.07454545454545455,2.350090679770045,0.2359677749953213,0.24233327983706834,1.2864543031768623,7.619582614368016,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.194730676024165,2.077935076750562,0.40661086507567396,0.8723008993810492,0.8723008993810492,2.427490211534449,0.0,0.0,0.0,0.0,0.0,46.76965046947548,0.0,0.0,0.0,0.0,0.0,15.929943897949348,0.0,0.0,0.0,0.8723008993810492,46.76965046947548,0.0,10.772448428929591,0.0,0.0,1.098621583211508,0.0,0.0,45.45726148307848,0.0,0.0,0.0,0.0,0.0,15.929943897949348,0.8723008993810492,4.901771144695251,0.27320076831237544,0.0,-0.010683922558922506,0.0,0.0,-0.21731829347900755,0.0,0.0,0.0,0.0,11,0,0,0,0,0,2,0,2,0,0,1,0,0,0,0,2,3.6023000000000014,4.695272727272729,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+Trichloroethylene,ClC=C(Cl)Cl,0,1.282716049382716,-0.06419753086419741,1.282716049382716,0.1975308641975302,0.09475501172296388,26.277800000000003,26.0762,25.9828766144,30,0,0.1176031867403948,-0.0903590844896965,0.1176031867403948,0.0903590844896965,0.36,0.4,0.4,3.675948764555208,11.772476622436802,1.0309401076758502,0.6267786838055364,0.8803360514166089,0.5285468820183673,0.26338934190276814,0.39016802570830456,0.07098038523709835,0.2986554414255808,0.014285714285714282,0.1285714285714286,0.0,0.0,0.12199999999999997,1.3609640474436813,0.7576506238859179,0.5653839479392624,4.6099999999999985,9.345871858170948,0.0,0.8982936348315855,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,6.960563934139509,0.0,1.1022507136159796,0.274151711234044,0.274151711234044,6.960563934139509,0.0,0.0,0.0,0.0,0.0,10.002721742237824,0.0,0.0,0.0,0.0,0.0,34.802819670697545,0.0,0.0,0.0,0.274151711234044,10.002721742237824,0.0,0.0,0.0,0.0,0.0,0.0,0.0,10.002721742237824,0.0,0.0,0.0,0.0,0.0,0.0,7.234715645373553,1.2432098765432102,2.9543209876543206,0.0,0.0,-0.06419753086419741,0.0,0.0,0.0,0.0,0.0,0.0,5,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,2.5017000000000005,5.1284,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+Cyclohexanol ,OC1CCCCC1,0,1.0794642857142858,-0.5138392857142858,1.0794642857142858,3.5708333333333337,0.06947498342054466,14.308714285714276,12.580714285714283,14.298402143428572,42,0,0.21044493502697398,-0.39313138246784024,0.39313138246784024,0.21044493502697398,0.22448979591836735,0.32653061224489793,0.40816326530612246,3.7616554503537105,50.811549542712804,2.243872397312364,2.2011783272091234,0.486892612923409,1.1515228816828316,1.0874817765279707,0.2434463064617045,0.13630344931884736,0.13630344931884736,0.06815172465942368,0.06815172465942368,0.03407586232971184,0.03407586232971184,-0.005714285714285714,2.244719052146579,0.09824875439680758,0.3305867456449512,1.4750627509406329,8.756954351231485,0.7298297415867446,0.0,0.0,0.2044570938903763,0.0,0.0,0.0,0.0,0.0,0.0,2.7312535291411626,1.8208356860941084,1.9582265088145998,1.0643966547314583,2.8838789012828046,0.0,1.4311996572326342,0.0,0.0,5.420663219085269,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.8028608393271193,0.0,0.0,6.706138374931336,0.0,0.0,0.0,0.0,2.89,5.420663219085269,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.1088081911072125,2.3585062535864365,12.687857142857144,0.0,0.0,0.5276785714285714,0.0,0.0,0.0,-3.072678571428572,0.0,0.0,1.0,7,1,1,1,0,1,0,0,0,1,1,1,1,1,0,1,1,1.3114,4.155971428571426,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+2-Methyl-1-Pentene,CCCC(=C)C,0,1.2336033950617284,-0.5884452160493829,1.2336033950617284,1.4508101851851853,0.0750704687606988,14.026999999999989,12.010999999999997,14.015650063999999,36,0,0.05371744828358774,-0.10014110520911675,0.10014110520911675,0.05371744828358774,0.3888888888888889,0.5555555555555556,0.638888888888889,6.010320112964545,56.09326436507909,2.5257834230632086,2.5,0.5,1.2608973568170352,1.2083333333333333,0.20833333333333334,0.10416666666666667,0.10416666666666667,0.03125,0.03125,0.010416666666666666,0.010416666666666666,-0.043333333333333335,1.9512076606644553,0.0767019522702261,0.4918284106891703,3.7400000000000007,9.455667428273687,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,6.531038962001867,3.132986924782388,2.2041361697708433,2.284597593617033,0.45691951872340675,2.7415171123404396,0.0,0.0,0.0,0.0,4.4082723395416865,0.0,12.104143492071135,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,7.149789451882129,12.104143492071135,0.0,0.0,0.0,0.0,6.4256295882202075,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.7415171123404396,14.15860339506173,0.0,0.0,0.0,-0.2436535493827159,0.0,0.0,-1.1690779320987656,-1.3569830246913581,0.0,0.6666666666666666,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,2.3626000000000005,4.953666666666664,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+p-Nitroanisole,COc1ccc(cc1)N(=O)=O,0,0.9627755982443482,-0.2701467266199409,0.9627755982443482,0.8374074074074069,0.04351117236334907,13.921545454545459,13.280090909090909,13.912963007636364,58,0,0.2691952053150494,-0.49675973475200186,0.49675973475200186,0.2691952053150494,0.1487603305785124,0.2066115702479339,0.24793388429752064,3.536618573317064,43.976015677371294,1.2953235150467206,1.1065416788083224,0.47017804244468614,0.7326578754116568,0.545182132716723,0.22700031453490482,0.13971843642572046,0.13971843642572046,0.07940924817654334,0.07940924817654334,0.03970462408827166,0.03970462408827166,-0.14363636363636365,2.198478748741359,0.22518325631931477,0.2732020949300233,1.6689819002348796,6.672514517006811,0.43062390489091357,0.5226828939349004,0.0,0.0,5.687386274683562,0.0,0.9194834789786884,0.0,0.0,0.0,0.0,1.098621583211508,1.098621583211508,1.9596884909901633,1.7504985359826597,0.5170351158803238,0.0,0.0,0.0,0.0,7.037952458882589,34.28399309941875,0.0,5.749511833283905,0.43062390489091357,5.687386274683562,5.749511833283905,0.0,1.0873875916091145,0.0,10.114318268765572,0.8723008993810492,24.16967483065318,0.0,0.0,0.0,4.760909090909091,4.324348767847355,0.9194834789786884,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.736862953800049,0.8723008993810492,4.926961064728922,0.0,0.8655412221706864,0.9627755982443482,-0.09319092283377996,-0.07612794612794609,-0.3385395622895622,0.0,0.0,-0.2701467266199409,0.14285714285714285,11,0,4,0,0,0,1,0,1,3,0,4,3,0,0,0,1,1.6033999999999997,3.6044000000000014,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
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+"2,6-Dimethylpyridine",Cc1cccc(C)n1,0,0.9224103009259259,-0.3471498842592593,0.9224103009259259,0.7333333333333332,0.061423472114704517,13.394499999999994,12.260499999999999,13.384187411,42,0,0.06418518299543609,-0.25848808574390264,0.25848808574390264,0.06418518299543609,0.1875,0.265625,0.328125,3.6272141987154662,50.73478852388136,1.6992322658918346,1.6184016994374948,0.49340169943749473,0.9324038738774454,0.8059016994374948,0.24340169943749473,0.14800212429686843,0.14800212429686843,0.07317627457812106,0.07317627457812106,0.039669599718747374,0.039669599718747374,-0.10625000000000001,2.0968576182755445,0.12960484381140436,0.2435327644898325,1.0594030875809968,7.753208728843379,0.0,0.0,0.0,0.0,0.0,0.0,0.6229973151184011,0.0,0.0,0.0,0.7553023384579118,3.2235777062397437,2.45155091583978,0.5140344585638326,1.5421033756914975,0.0,0.0,0.6229973151184011,0.0,1.7129730293239198,0.0,29.515112112686808,0.0,0.0,0.0,0.0,0.0,0.0,0.6229973151184011,0.0,13.703784234591359,2.9655853744036125,18.127256122989884,0.0,0.0,0.0,1.61125,5.40236204340977,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.9839785209472085,1.5421033756914975,8.094635416666668,0.0,0.41939814814814813,0.0,-0.18990162037037028,0.0,-0.27566550925925914,0.0,-0.6942997685185185,0.0,0.2857142857142857,8,0,1,0,0,0,0,1,1,1,0,1,2,0,0,0,1,1.69844,4.213874999999999,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0
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+1-Chloropentane,CCCCCCl,0,1.2125231481481482,-0.6070601851851852,1.2125231481481482,3.2548611111111105,0.06383251281376541,17.76599999999999,15.918,17.675821338666665,38,0,0.043519416708896266,-0.12671697108785232,0.12671697108785232,0.043519416708896266,0.3055555555555555,0.4444444444444444,0.5277777777777778,5.733228775936007,42.536227624944324,2.4166666666666665,2.3129940788348713,0.6056489031712803,1.1666666666666667,1.114830372750769,0.26115778491897346,0.07824851970871781,0.1097455591261534,0.028707593187692237,0.04445611289641003,0.009145463260512783,0.017019723114871686,0.04833333333333333,1.9839765572031232,0.11055250063340645,0.8816666666666667,4.29,9.992826869023938,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,11.600939890232516,3.266290319992406,1.0621541502215632,3.485075140207688,0.0,2.5130573529787363,1.9334899817054192,0.0,0.0,0.0,4.328444470213969,5.832106723373709,0.0,0.0,0.0,0.0,0.0,0.0,11.600939890232516,0.9720177872289515,0.0,0.0,6.841501823192709,0.0,0.0,0.0,0.0,0.0,5.300462257442921,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.446547334684156,12.966183206307214,0.8389119583018391,0.0,0.0,0.0,-0.5424768518518518,0.0,-1.7937011316872429,-0.5753986625514406,0.0,1.0,6,0,0,0,0,0,0,0,0,0,0,1,4,0,0,0,0,2.415400000000001,5.040833333333331,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
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+"2,4'-PCB",Clc1ccc(cc1)c2ccccc2Cl ,0,0.5651809559802757,-0.04092518356548969,0.5651809559802757,0.2765432098765428,0.04706794109303885,15.935857142857145,15.35985714285714,15.857164686857143,70,0,0.06386294087483058,-0.0843437391002565,0.0843437391002565,0.06386294087483058,0.061224489795918366,0.10714285714285714,0.15816326530612246,3.026914071477952,48.369935348934256,1.2091573735911079,1.0539949247156037,0.5905562027182402,0.721916858944971,0.5448546052149448,0.313135244216263,0.1698546052149448,0.2238495299305487,0.1061591282593405,0.13990595620659294,0.0636954769556043,0.08394357372395578,-0.06999999999999999,2.499719362818697,0.2771132262718806,0.28821633798029217,1.9176574475475268,7.452485270047629,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.8152838084843936,1.6921934010490627,1.1149084370842606,0.78329060352584,0.78329060352584,1.6572771271760736,0.0,0.0,0.0,0.0,0.0,58.38461628878901,0.0,11.126902983393991,0.0,0.0,0.0,23.20187978046503,0.0,0.0,0.0,0.78329060352584,48.33934966130636,10.045266627482652,11.126902983393991,0.0,0.0,3.4528106900933113,0.0,0.0,21.172169610876644,0.0,0.0,0.0,0.0,0.0,0.0,2.440567730701914,4.431556472383192,0.8344160847760619,0.0,-0.05225985014817204,-0.0418165784832451,0.0,-0.29888025551196357,0.0,0.0,0.0,0.0,14,0,0,0,0,0,2,0,2,0,0,2,1,0,0,0,2,4.660400000000002,4.421285714285716,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+"Etoposide (148-167,25mg/ml)",COc1cc(cc(OC)c1O)C6C2C(COC2=O)C(OC4OC3COC(C)OC3C(O)C4O)c7cc5OCOc5cc67,0,0.3485435862587913,-0.12237867762265657,0.3485435862587913,1.4270083262703321,0.010234029963009483,14.013380952380988,13.245380952380959,14.004387882952399,226,0,0.3099403857906003,-0.5016994790132149,0.5016994790132149,0.3099403857906003,0.02437641723356009,0.04308390022675737,0.060090702947845805,1.708447446145564,64.29387727198412,1.3569246398500883,1.2335054232388147,0.4716006613340529,0.783703876894507,0.6360827634879543,0.2616840760738689,0.193086429050573,0.193086429050573,0.13912424957299532,0.13912424957299532,0.09799499324565288,0.09799499324565288,-0.07928571428571428,3.6382281873230973,0.22226198853061283,0.24847146960060917,4.258657173456175,6.79639415963341,1.3799569323219545,0.6443235528604855,0.8459572397668909,0.36564182695838976,1.4311996572326342,5.969305287951849,0.11415564723980529,0.0,0.0,0.0,0.0,1.1359981291199963,0.47648730256541605,1.6179471617972734,2.4405887254888134,0.14212631637980594,4.293598971697903,0.0,11.787915370726157,1.325668968440059,33.93892098810095,40.86002930574416,0.0,28.747559166419524,0.4511298051238142,0.0,28.747559166419524,0.0,2.294773301570145,0.678067903644573,11.787915370726157,1.7921004865151464,24.16967483065318,0.0,0.0,0.0,3.8292857142857146,4.2138530188910766,0.4405267320422386,0.0,0.0,0.0,0.0,0.0,0.0,0.0,20.063287527121684,1.6245932183174412,7.62259033169223,0.0,0.3485435862587913,0.3029719699300128,-0.14273378043025436,-0.5969472229145005,-0.1776389045016929,-0.8031175671633699,-0.4024464658023043,-0.1829679788149475,0.5517241379310345,42,3,13,1,4,5,2,0,2,13,3,13,11,0,3,3,7,1.338599999999999,3.293938095238094,0,2,0,0,0,0,0,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,4,0,0,0,1,9,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
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+Cyclooctanol,OC1CCCCCCC1,0,0.8678240740740741,-0.4561342592592595,0.8678240740740741,3.7016921768707483,0.05881490048149632,14.246111111111102,12.454111111111109,14.235568348,54,0,0.21044493502697376,-0.3931313824680339,0.3931313824680339,0.21044493502697376,0.1358024691358025,0.19753086419753085,0.2592592592592593,4.143767762200193,56.259774385295394,2.3007896423540606,2.267583143384874,0.4898053656070959,1.1734066857533134,1.1235969372995327,0.24490268280354796,0.13379157169243683,0.13379157169243683,0.06689578584621841,0.06689578584621841,0.03344789292310921,0.03344789292310921,-0.0044444444444444444,2.4300944983358934,0.1012517349696837,0.43188722762536097,2.8502759632909154,8.8498274097872,0.567645354567468,0.0,0.0,0.15902218413695934,0.0,0.0,0.0,0.0,0.0,0.0,3.5405138340718776,1.416205533628751,2.1322910873758976,0.8278640647911343,2.852242948184501,0.0,1.4311996572326342,0.0,0.0,5.6322769262506265,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.4022250972544261,0.0,0.0,7.241316961317667,0.0,0.0,0.0,0.0,2.247777777777778,5.6322769262506265,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.1088081911072125,2.4436197777539928,13.612343159486018,0.0,0.0,0.41129913076341645,0.0,0.0,0.0,-3.634753401360545,0.0,0.0,1.0,9,1,1,1,0,1,0,0,0,1,1,1,1,1,0,1,1,2.0915999999999997,4.258422222222222,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+Methyl t-butyl ether ,COC(C)(C)C,0,1.1777777777777778,-0.6276041666666666,1.1777777777777778,3.4750000000000014,0.07249789672999273,14.691666666666656,12.675666666666665,14.681469167333333,38,0,0.05942520764405295,-0.3791035972502146,0.3791035972502146,0.05942520764405295,0.25,0.3611111111111111,0.3888888888888889,6.097880982711177,41.91857179204029,2.534517796864425,2.484708048410644,0.48470804841064385,1.242851130197758,1.1930413817439771,0.1930413817439772,0.1305413817439772,0.1305413817439772,0.025515518153991442,0.025515518153991442,0.0,0.0,-0.006666666666666667,1.8990671321187291,0.08495846683279933,0.26760998810939357,3.9600000000000004,9.362035119489443,0.7894771589666748,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.4259460586478396,3.229129910735761,1.6188877465823914,3.5309942713071147,0.0,0.0,0.0,0.0,4.359454527145121,7.037952458882589,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.1065005449777128,0.7894771589666748,0.0,6.167463170988281,0.0,0.0,0.0,0.0,1.5383333333333333,5.532446603625552,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.736862953800049,2.7415171123404396,14.66614583333333,0.0,0.0,0.0,-0.6276041666666666,0.0,0.0,0.0,-1.8343750000000003,-0.5791666666666669,1.0,6,0,1,0,0,0,0,0,0,1,0,1,1,0,0,0,0,1.4313,4.460333333333331,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+Acrolein,C=CC=O,0,2.4416203703703703,-0.32175925925925913,2.4416203703703703,0.87037037037037,0.07828281611069988,14.016000000000004,13.008000000000001,14.006553687,22,0,0.14174582194221017,-0.29858708973922715,0.29858708973922715,0.14174582194221017,0.625,0.8125,0.8125,4.2266192868964705,33.55471227508702,1.6830127018922192,1.4770620726159658,0.4770620726159658,0.8883545031536988,0.6760310363079829,0.1760310363079829,0.056765518153991446,0.056765518153991446,0.012757759076995721,0.012757759076995721,0.0,0.0,-0.14750000000000002,1.6403360647604435,0.1205070385342715,0.6025000000000001,1.4100000000000017,7.640234081288382,0.0,1.9082426796253653,0.0,0.0,0.0,0.0,1.1986342960179555,0.0,0.0,0.0,1.6327597405004668,1.5130179365088916,0.0,1.0280689171276651,2.5693928521881757,1.5655530405828102,0.0,0.0,0.0,0.0,0.0,12.583110708037434,0.0,0.0,0.0,0.0,0.0,0.0,1.5655530405828102,1.1986342960179555,0.0,1.3707585561702202,12.583110708037434,0.0,0.0,0.0,4.2675,4.711330717592169,1.1986342960179555,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.3707585561702202,6.325740740740741,0.0,2.4416203703703703,0.0,0.0,0.0,-0.2175925925925925,-0.32175925925925913,-0.22800925925925924,0.0,0.0,4,0,1,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0.37129999999999996,4.06525,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+triamcinolone diacetate,CC(=O)OCC(=O)C3(O)C(CC4C2CCC1=CC(=O)C=CC1(C)C2(F)C(O)CC34C)OC(C)=O ,0,0.5934811528586793,-0.1875872196968269,0.5934811528586793,2.258098260679378,0.017220766232808833,14.073911764705873,13.154852941176479,14.064714593294141,186,0,0.30256569469468375,-0.4589531200440186,0.4589531200440186,0.30256569469468375,0.04065743944636678,0.06487889273356402,0.0847750865051903,2.696454702297699,65.03259651617736,1.5278596846547998,1.3865867881388274,0.47482208225647443,0.832071613691127,0.7072564785221075,0.2567712849654097,0.2069781567209109,0.2069781567209109,0.1537363025896593,0.1537363025896593,0.10819765904442875,0.10819765904442875,-0.07029411764705883,3.2237117917235105,0.20236349081515137,0.22978294146670686,3.086801125193764,7.072057025965482,0.5791571261710153,0.17882405961617612,0.6944717887944292,0.25428365473030023,0.0,11.938610575903699,0.5640631981260967,4.39041504767482,0.0,0.0,0.5434431880411916,1.4806380264719825,1.7818944439556734,0.4610390564747508,2.441526594197441,0.6913264843715758,2.8623993144652684,0.0,22.61789630951972,2.244973963474399,6.558985242916289,23.729319768175966,0.0,0.0,0.0,4.39041504767482,0.0,0.0,1.9580572968193797,0.8427021954084524,22.61789630951972,2.7250372412775454,23.729319768175966,0.0,0.0,0.0,3.7411764705882358,4.4923651925698564,0.7455788235758006,0.27014565387490125,0.0,0.0,0.0,0.0,0.0,0.0,19.69134228981452,0.9308332057070555,8.669792505203764,0.0,1.5895623589938865,0.24207494383390019,-0.781623574438579,-0.7192932004066771,-0.21247148002315322,-1.0133135709932637,-0.7281593547188985,0.0,0.68,34,2,8,4,0,4,0,0,0,8,2,9,10,3,0,3,4,1.7620999999999996,3.408282352941178,0,2,0,0,0,0,0,0,0,0,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,0,0,5,0,0,0,2,2,0,0,1,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+Pyridazine,c1ccnnc1,0,1.164567901234568,-0.06003086419753086,1.164567901234568,0.3472222222222221,0.07542619051633219,13.34833333333333,12.676333333333332,13.339574687999999,30,0,0.08598140941410494,-0.15917583224964102,0.15917583224964102,0.08598140941410494,0.19444444444444445,0.2777777777777778,0.3611111111111111,3.3421538077705124,39.1132763960928,1.287269106521933,1.149071198499986,0.48240453183331927,0.7709829429477049,0.5662022659166596,0.2328689325833263,0.11226779962499649,0.11226779962499649,0.054050566479164903,0.054050566479164903,0.02598361657291579,0.02598361657291579,-0.15333333333333335,1.9321601728882982,0.17094684546566016,0.24996713993427988,0.5163568727137455,6.909839108410996,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,10.197363616602075,0.0,0.0,2.0141395692210984,2.0576317369382537,0.9138390374468135,0.9138390374468135,0.0,0.0,1.6995606027670125,0.0,0.0,0.0,24.430627836956113,0.0,0.0,0.0,0.0,0.0,0.0,1.6995606027670125,0.0,0.0,0.9138390374468135,24.430627836956113,0.0,0.0,0.0,4.296666666666667,0.0,0.0,0.0,24.430627836956113,0.0,0.0,0.0,0.0,0.0,10.197363616602075,0.9138390374468135,4.626666666666666,0.0,0.0,1.0535802469135802,0.0,0.0,-0.1157407407407407,-0.12006172839506173,0.0,0.0,0.0,6,0,2,0,0,0,0,1,1,2,0,2,0,0,0,0,1,0.4765999999999999,3.6719999999999993,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+4-Chlorotoluene,Cc1ccc(Cl)cc1,0,0.9242505787037038,-0.3345293209876543,0.9242505787037038,0.3629629629629625,0.06268770825084556,15.823249999999994,14.941249999999998,15.752953488,42,0,0.06385012839856423,-0.08434413556465584,0.08434413556465584,0.06385012839856423,0.1875,0.265625,0.328125,3.6807357198826742,47.471916475878245,1.4955127018922194,1.3597455591261534,0.5792366773784602,0.834428310067868,0.6798727795630767,0.2896183386892301,0.14862277956307668,0.1958683386892301,0.07431138978153834,0.09793416934461505,0.03715569489076918,0.04896708467230752,-0.061250000000000006,2.094639700632517,0.18896314229450825,0.27317010236609646,1.2392064029119825,7.991800620243745,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.6561535996570127,2.3670911915777837,1.1418636227814984,0.6853792780851101,1.1994137366489426,1.4501174862790644,0.0,0.0,0.0,0.8564865146619599,0.0,34.7557596360915,0.0,0.0,0.0,0.0,0.0,11.600939890232516,0.0,0.0,6.851892117295679,1.8948451731110667,24.16967483065318,5.022633313741326,0.0,0.0,0.0,4.573127304955731,0.0,0.0,5.022633313741326,0.0,0.0,0.0,0.0,0.0,0.0,2.649531222928007,6.344134897014362,0.6889495937263793,0.0,-0.04537037037037031,-0.0744328703703703,0.0,-0.2836130401234567,0.0,-0.3345293209876543,0.0,0.14285714285714285,8,0,0,0,0,0,1,0,1,0,0,1,1,0,0,0,1,2.6484200000000007,4.523625000000001,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+2-Chlorobutane,CCC(C)Cl,0,1.4207947530864198,-0.6625385802469136,1.4207947530864198,3.1892361111111116,0.08687512049005953,18.513799999999993,16.6994,18.407855593599997,32,0,0.04686899348538178,-0.12332141991372317,0.12332141991372317,0.04686899348538178,0.52,0.6799999999999999,0.72,5.300082077607573,37.16645878435746,2.4,2.2755928946018456,0.6267786838055363,1.15,1.0877964473009227,0.26338934190276814,0.08779644730092273,0.16338934190276816,0.02194911182523068,0.04084733547569204,0.0,0.0,0.057999999999999996,1.8096798835528565,0.11024281397612219,0.5046200466200467,4.289999999999999,10.156415619882278,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,11.600939890232516,1.3703784234591359,2.644963403725012,3.5379933025878776,0.0,2.467365401106396,2.320187978046503,0.0,0.0,0.0,5.08596972866563,0.0,0.0,0.0,0.0,0.0,0.0,0.0,11.600939890232516,1.070627901481482,0.0,0.0,6.482707228290543,0.0,0.0,0.0,0.0,0.0,5.08596972866563,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.787553379152898,12.38601697530864,1.031560185185185,0.0,-0.6378472222222223,0.0,0.0,0.0,-0.6625385802469136,-1.2949691358024693,0.0,1.0,5,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,2.0237,5.121199999999997,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+Hexylbenzene ,CCCCCCc1ccccc1,0,0.6721279368228773,-0.34918179366654084,0.6721279368228773,0.9285309108530153,0.0481208675639106,13.523000000000012,12.010999999999997,13.511737548,66,0,0.06256033944053933,-0.06538238174359431,0.06538238174359431,0.06256033944053933,0.10416666666666667,0.17361111111111113,0.23611111111111113,3.8743316190381134,66.79534897253417,2.038675134594813,2.0,0.5,1.0771188733785786,1.0,0.25,0.13541666666666666,0.13541666666666666,0.07291666666666667,0.07291666666666667,0.0390625,0.0390625,-0.065,2.559621506674956,0.1143807603673143,0.48482954794042304,3.5646069197864145,8.409210400741431,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.681896696633358,1.5257751078629795,1.4849884358510714,0.5711493984042585,2.05613783425533,0.0,0.0,0.0,0.0,3.226376385328548,0.0,35.77554503001347,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5310770751107816,0.0,5.215058102114518,30.212093538316473,0.0,0.0,0.0,0.0,6.207671804496338,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.05613783425533,11.518692524796817,0.0,0.0,0.0,-0.1093657486142605,0.0,-0.4528196084273035,-1.6847954022143845,-0.3133784322075344,0.0,0.5,12,0,0,0,0,0,1,0,1,0,0,0,6,0,0,0,1,3.809400000000003,4.524000000000004,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+1-Chloroheptane,CCCCCCCCl,0,0.945534336419753,-0.511701388888889,0.945534336419753,3.47953691893424,0.05003906099322773,16.83125,14.941249999999998,16.76077852,50,0,0.04351941808357943,-0.12671696964453744,0.12671696964453744,0.04351941808357943,0.171875,0.265625,0.34375,6.370990656665334,49.96527438819279,2.4375,2.3597455591261536,0.5792366773784602,1.1875,1.1486227795630768,0.2583683386892301,0.08993638978153835,0.11355916934461505,0.03715569489076918,0.04896708467230752,0.014671597445384588,0.02057729233615376,0.03625,2.2407403893236806,0.11084075190789221,0.9112499999999994,6.289999999999997,9.788340930451005,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,11.600939890232516,4.04294896532665,0.7966156126661724,3.2991856332408758,0.0,2.570172292819162,1.4501174862790644,0.0,0.0,0.0,4.839564577992822,5.832106723373709,0.0,0.0,0.0,0.0,0.0,0.0,11.600939890232516,0.7290133404217136,0.0,0.0,7.40973687081199,0.0,0.0,0.0,0.0,0.0,5.568577918414535,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.020289779098227,13.72663364545278,0.6367509543362097,0.0,0.0,0.0,-0.43494211486678,0.0,-2.4848198144998745,-0.460983781533447,0.0,1.0,8,0,0,0,0,0,0,0,0,0,0,1,6,0,0,0,0,3.1956000000000016,4.934875000000001,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+3-Pentanoyloxymethylphenytoin,O=C1N(COC(=O)CCCC)C(=O)C(N1)(c2ccccc2)c3ccccc3,0,0.5324647885567346,-0.16352154616844342,0.5324647885567346,0.6210349035734599,0.022340281334863675,13.570999999999989,12.74966666666667,13.561405821629652,140,0,0.32786640076668144,-0.4435958605585316,0.4435958605585316,0.32786640076668144,0.0438957475994513,0.06584362139917695,0.08641975308641976,2.4131793159605692,61.53449624791192,1.4267430132374146,1.2973118649205693,0.4824970501057544,0.8083983798992882,0.6605877152003522,0.25513535981146496,0.1704132291284157,0.1704132291284157,0.116575276301774,0.116575276301774,0.07695661518291537,0.07695661518291537,-0.11666666666666661,3.169611288447935,0.20143417018889948,0.297543411732642,3.556381478076956,6.986754082258555,0.3720991224847565,0.0,0.5049513835783112,0.0,5.907179729351506,12.000419800289922,0.35515090252383863,9.694446914922299,0.0,0.0,2.7277408924169637,0.648141773508273,0.6929537743281984,0.6092260249645421,1.8266017134784578,0.6632444270237565,0.0,0.37813789200514275,0.0,1.1670219286543395,6.682920025902854,71.55109006002695,0.0,0.0,0.1966597538254954,4.794537184071822,0.0,0.0,1.0922381217183244,0.7357356509010012,0.0,2.44012961052787,60.42418707663295,1.4118420783282006,0.0,0.0,2.804074074074074,5.105960176791751,0.5327263537857581,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.736862953800049,1.1184359910334376,6.641983425900621,0.0,1.4833982365177831,-0.02300129272494296,-0.24367107706203017,-0.18622675784117407,-0.5526567316497687,-0.4785971667569625,-0.3078953030501894,0.0,0.2857142857142857,27,1,6,0,1,1,2,0,2,4,1,6,8,0,1,1,3,3.172900000000002,3.6861370370370383,0,0,0,0,0,0,0,0,0,0,3,3,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,2,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1
+Methaqualone,Cc1ccccc1n3c(C)nc2ccccc2c3=O,0,0.7084547181445676,-0.17072496469347978,0.7084547181445676,0.17602088057445187,0.03500636164102657,13.173736842105267,12.430999999999997,13.163716477263158,94,0,0.2654677569072256,-0.2681674320933315,0.2681674320933315,0.2654677569072256,0.05817174515235458,0.09972299168975068,0.1440443213296399,2.7693762072875114,72.96827950909301,1.335124253633207,1.2264566042875673,0.48961449902440946,0.7854385582188061,0.6353767438937803,0.2669556912622014,0.18543087121886206,0.18543087121886206,0.12660428709715235,0.12660428709715235,0.08365540191769132,0.08365540191769132,-0.12736842105263155,2.6914058359136095,0.19955548621686212,0.2291608247823854,1.8340679152322232,6.862127507208993,0.0,0.30654760515789087,0.0,0.0,5.559266895052007,0.0,0.4927177279927988,4.9839785209472085,0.0,0.0,1.590110186227183,1.9681311499620127,0.4328711230011222,1.4503357713750724,1.0100326203359515,0.5738381543200556,0.0,0.5026883246704507,0.0,0.7212518018205978,0.0,70.08100973012712,0.0,5.687386274683562,0.2925929944764214,0.0,0.0,0.0,0.5026883246704507,0.0,13.703784234591359,1.6093934618989472,53.13388684537818,0.0,16.59031120676462,0.0,1.8363157894736843,5.030556209863751,0.2523440623195696,0.0,4.567099647791355,0.0,0.0,0.0,0.0,0.0,4.9839785209472085,1.0100326203359515,5.805396677989512,0.0,0.9055760432828099,-0.04030686836137962,-0.21708952993153077,-0.059183176194454366,-0.3787497690228712,0.0,-0.34020478127085807,0.0,0.125,19,0,3,0,0,0,2,1,3,3,0,3,3,0,0,0,3,3.0025400000000015,4.042842105263159,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+m-Xylene ,Cc1cccc(C)c1,0,0.9491030092592593,-0.3579282407407407,0.9491030092592593,0.7886574074074069,0.05946971066754013,13.270999999999992,12.010999999999997,13.25978129,42,0,0.06283107966352146,-0.06174087142677341,0.06283107966352146,0.06174087142677341,0.171875,0.25,0.3125,3.834543489724933,54.88960951404367,1.8080127018922192,1.75,0.5,0.9858439182435164,0.875,0.25,0.15625,0.15625,0.078125,0.078125,0.04296875,0.04296875,-0.0975,2.0941531485135068,0.11775020874897647,0.24921808903590698,1.0935372033822537,8.026374043746042,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.412072226755896,1.7129730293239198,1.0280689171276651,0.6853792780851101,1.713448195212775,0.0,0.0,0.0,0.0,1.7129730293239198,0.0,35.29657781404717,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,13.703784234591359,3.1043110681370236,24.16967483065318,0.0,0.0,0.0,0.0,6.125045256079816,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.713448195212775,9.133738425925925,0.0,0.0,0.0,-0.19916087962962953,0.0,-0.4062210648148147,0.0,-0.7158564814814814,0.0,0.25,8,0,0,0,0,0,1,0,1,0,0,0,2,0,0,0,1,2.30344,4.4895,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+Talbutal,CCC(C)C1(CC=C)C(=O)NC(=O)NC1=O,0,0.8157730418556309,-0.29532696759259275,0.8157730418556309,0.9467243008314434,0.03446147121160327,14.016250000000007,13.008249999999997,14.00725577325,88,0,0.3276324784995045,-0.27680308492813477,0.3276324784995045,0.27680308492813477,0.109375,0.15234375,0.18359375,4.538823638574631,52.96474410336483,1.6275907427704612,1.4761982538994691,0.4761982538994691,0.8688433889980071,0.7282016761059767,0.23479997666848196,0.16353038202512507,0.16353038202512507,0.11162337494893194,0.11162337494893194,0.06429477252944758,0.06429477252944758,-0.10312500000000001,2.614341880029089,0.17353099580946071,0.27918973051461915,1.7271946728041057,7.304573120036949,0.0,0.3384369043372987,0.17648025979102508,0.7383974661689382,0.0,6.031114512338072,1.2630438171700247,4.794537184071822,0.0,6.531038962001867,1.633048805122269,0.7666801616682786,0.9423965073670262,0.2570172292819163,2.274869718453434,1.1153421231900678,0.0,0.663726669161047,11.308948154759857,1.6531021273281323,0.0,12.583110708037434,0.0,0.0,0.663726669161047,4.794537184071822,0.0,0.0,1.1153421231900678,0.5993171480089777,11.308948154759857,2.8525158639770765,12.583110708037434,2.8236841566564013,0.0,0.0,4.704375000000001,4.925424598604078,0.8989757220134666,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.3758939964399672,7.6531206055917735,0.0,2.372838322684397,-0.11834053760393043,-0.28788483796296305,-0.6157841435185187,-0.25440924981103546,-0.5518021305744523,-0.6456546954719391,0.0,0.5454545454545454,16,2,5,0,1,1,0,0,0,3,2,5,6,0,1,1,1,0.9609999999999999,3.6489625000000014,0,0,0,0,0,0,0,0,0,0,3,3,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1
+Iodofenphos,COP(=S)(OC)Oc1cc(Cl)c(I)cc1Cl,0,0.49158403486394564,-0.27709518298059965,0.49158403486394564,0.15129432791635167,0.02583144439997098,25.812499999999993,25.308500000000002,25.739709694125015,90,0,0.3795716380340057,-0.42248134615232347,0.42248134615232347,0.3795716380340057,0.0859375,0.125,0.16015625,3.428693762278984,40.522870348050205,1.1928388724185874,0.9508812610239432,0.7871487582202479,0.6601729118361477,0.463100993034366,0.48752078636545304,0.14013410806239457,0.42878839722561785,0.07884517750751192,0.2789531489468825,0.04050081619022839,0.14219290897200632,0.056249999999999994,2.3454732743328477,0.43068750907243436,0.3862370012779888,3.6983453449981365,8.374374107260827,0.8482025928209658,0.35934448958024406,0.0,0.0,0.0,6.718607232363179,0.0,0.0,0.0,0.0,1.4501174862790644,1.7895805834207095,2.2184601240707824,1.3132084423027757,1.5335818709060756,4.019888358033432,0.0,0.0,0.0,0.0,14.075904917765177,25.70028631387464,0.0,5.749511833283905,0.2827341976069886,0.0,5.749511833283905,52.51135763989627,0.8797440573603236,1.3033969007538926,3.5701822710653963,0.68537927808511,12.08483741532659,10.045266627482652,0.0,0.0,1.730625,2.350567250301055,0.0,0.0,14.635234292470017,0.0,0.0,0.0,22.590870627068057,0.0,9.047494323423635,3.1561594675110802,4.517894901633493,1.1411176843847106,0.0,-0.03844756826341643,0.0,-0.0370997685185185,-0.05137966702884855,0.0,-0.27709518298059965,-0.39158762144904274,0.25,16,0,3,0,0,0,1,0,1,4,0,8,6,0,0,0,1,4.494100000000002,4.881875000000001,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
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+1-Bromonapthalene,Brc1cccc2ccccc12,0,0.7074242424242424,-0.039980588194873906,0.7074242424242424,0.033449074074073826,0.05623226114894806,18.824545454545458,18.183090909090904,18.72482839309091,54,0,0.0634822091776175,-0.06162840710618613,0.0634822091776175,0.06162840710618613,0.09090909090909091,0.15702479338842976,0.23966942148760328,3.2161246449441516,51.56374976119334,1.2521366083542054,1.1252694975462934,0.6330873647385392,0.7532244381985159,0.5853620215004194,0.3392709550965423,0.17627111240951032,0.24836186418745143,0.11515787976167821,0.16922594359513402,0.07109010165930062,0.10713547754827117,-0.07454545454545455,2.350090679770045,0.2359677749953213,0.24233327983706834,1.1621153719595982,7.619582614368017,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.74404146762992,1.528624285144808,0.40661086507567396,0.8723008993810492,0.8723008993810492,2.427490211534449,0.0,0.0,0.0,0.0,0.0,46.76965046947548,0.0,0.0,0.0,0.0,0.0,15.929943897949348,0.0,0.0,0.0,0.8723008993810492,46.76965046947548,0.0,10.772448428929591,0.0,0.0,1.098621583211508,0.0,0.0,45.45726148307848,0.0,0.0,0.0,0.0,0.0,15.929943897949348,0.8723008993810492,4.8897932986325845,0.27915893630179345,0.0,0.0038615319865320315,0.0,0.0,-0.22584406995121273,0.0,0.0,0.0,0.0,11,0,0,0,0,0,2,0,2,0,0,1,0,0,0,0,2,3.6023000000000014,4.695272727272729,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+1-Nonyne ,CCCCCCCC#C,0,0.8514660493827161,-0.46004661123708757,0.8514660493827161,1.2689265636810279,0.04335543474746451,13.80299999999999,12.010999999999997,13.791688945777777,52,0,0.1238247565210833,-0.12010931578969078,0.1238247565210833,0.12010931578969078,0.1728395061728395,0.271604938271605,0.3580246913580247,6.527118385281662,61.59778078907362,2.323801506930344,2.2777777777777777,0.5,1.1734066857533134,1.1111111111111112,0.2222222222222222,0.09722222222222222,0.09722222222222222,0.041666666666666664,0.041666666666666664,0.017361111111111112,0.017361111111111112,-0.04888888888888889,2.3544400769369576,0.09793192110019287,0.8400000000000003,7.560000000000003,9.015282370838516,0.0,0.1523065062411356,0.0,0.0,0.0,0.0,0.0,0.0,0.0,12.319835853677588,3.593732413623689,0.7081027668143755,2.9927003604314084,0.0,2.4369040998581686,0.0,0.0,0.0,0.0,5.009937947252439,0.0,0.0,0.0,12.319835853677588,0.0,0.0,0.0,0.0,0.0,0.0,12.319835853677588,7.446842047110612,0.0,0.0,0.0,0.0,0.0,5.009937947252439,0.0,0.0,0.0,0.0,0.0,12.319835853677588,0.0,0.0,0.0,2.4369040998581686,13.149464294462641,0.0,0.0,0.0,0.0,0.1557552148945998,0.0,-2.4757052049214754,-0.41284763776909955,0.0,0.7777777777777778,9,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,2.980100000000002,4.68988888888889,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0
+eucalyptol,CC12CCC(CC1)C(C)(C)O2 ,0,0.7702651515151515,-0.387310606060606,0.7702651515151515,4.039583333333335,0.04736595695208639,14.023000000000014,12.373545454545452,14.012342290545455,64,0,0.06617640341770793,-0.3691300164664661,0.3691300164664661,0.06617640341770793,0.1322314049586777,0.1983471074380165,0.24793388429752064,3.3066235150020677,58.57635777621922,2.1551915255624134,2.128022571860351,0.49165893549671486,1.109736980107868,1.0825680264058057,0.26438620822398756,0.21267586439197822,0.21267586439197822,0.1259373057345299,0.1259373057345299,0.07920580078344132,0.07920580078344132,-0.0036363636363636364,2.3891149837925556,0.11011663404910478,0.18245744905734226,1.0992889094512,8.583189271169816,0.43062390489091357,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.721939422051602,2.243059455551269,1.0183728747243068,2.6736833604421824,0.0,0.0,0.0,5.893957685363079,5.204497961742902,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0183728747243068,0.43062390489091357,5.893957685363079,6.4291845425698675,0.0,0.0,0.0,0.0,0.8390909090909091,5.740312296775909,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.6736833604421824,13.389015151515157,0.0,0.0,0.0,-0.766666666666667,-0.387310606060606,0.0,-1.4861742424242435,-1.1125,0.0,1.0,11,0,1,1,2,3,0,0,0,1,0,1,3,1,2,3,3,2.7441000000000013,4.138818181818184,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+2-Methylphenanthrene,Cc1ccc2c(ccc3ccccc32)c1,0,0.5485474930713026,-0.19274195763696422,0.5485474930713026,0.26287792894935724,0.03104553899328871,12.817400000000005,12.010999999999997,12.8062600256,72,0,0.06320586110328505,-0.06162899271075562,0.06320586110328505,0.06162899271075562,0.05777777777777778,0.11111111111111112,0.17333333333333334,2.820143550725723,76.17682742799526,1.3721935845769841,1.3,0.5,0.8212107260423187,0.6833333333333333,0.2833333333333333,0.2,0.2,0.1375,0.1375,0.09375,0.09375,-0.1213333333333333,2.562759524134355,0.1764492525506516,0.20630822982060862,1.298346533052511,7.067954686625181,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.9963479907111097,1.8931192650103241,0.274151711234044,0.822455133702132,1.0966068449361759,1.4363264571906122,0.0,0.0,0.0,0.45679280781971193,0.0,59.94521986066665,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,6.851892117295679,1.467503611049309,54.38176836896965,0.0,21.544896857859182,0.0,0.0,4.453140798530821,0.0,0.0,21.544896857859182,0.0,0.0,0.0,0.0,0.0,0.0,1.0966068449361759,6.343279132898016,0.0,0.0,-0.0824579554043839,-0.04857492791355219,0.0,-0.38061540305422575,0.0,-0.19274195763696422,0.0,0.06666666666666667,15,0,0,0,0,0,3,0,3,0,0,0,1,0,0,0,3,4.301420000000003,4.412733333333335,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+Diosgenin,C1C(O)CCC2(C)CC3CCC4(C)C5(C)CC6OCC(C)CC6OC5CC4C3C=C21,0,0.34533193578357263,-0.2015410761526834,0.34533193578357263,2.8395690839977465,0.01771168808899146,13.820999999999978,12.409800000000006,13.810446506800039,168,0,0.21046072330367727,-0.3927966863933903,0.3927966863933903,0.21046072330367727,0.04,0.07222222222222222,0.1,2.118969426846915,80.39527919279952,1.9258673627312966,1.8908248290463865,0.4908248290463863,1.025052758015344,0.9809623005541174,0.2840206908719886,0.23325609329252078,0.23325609329252078,0.1712085452172779,0.1712085452172779,0.12112262552494596,0.12112262552494596,-0.012666666666666666,3.2573174746921834,0.13775861347900234,0.21920437374648521,2.669865168589599,8.053997050838893,0.48608446995691035,0.0,0.0,0.04770665524108781,0.0,0.0,0.0,0.0,0.0,0.0,1.301091491509585,3.0587378597403427,1.9526588874033535,1.1305127333663831,2.3594544967228797,0.0,1.4311996572326342,0.0,39.82080214964265,3.636132156298236,6.558985242916289,11.625176276104835,0.0,0.0,0.0,0.0,0.0,0.0,1.2473021733018694,0.3157908635866699,39.82080214964265,4.698833112804199,11.625176276104835,0.0,0.0,0.0,1.2896666666666665,5.569630945253697,0.7767631818297912,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.1088081911072125,1.4601043637639346,13.079868649692736,0.0,0.0,0.13321322705738808,-0.6705985007429492,-0.74558854011625,-0.10165879067782777,-2.341863808079555,-0.8505944593557638,0.0,0.9259259259259259,30,1,3,4,2,6,0,0,0,3,1,3,5,3,2,5,6,5.508800000000006,3.933293333333337,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,7,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+"5,6,7,8-tetrahydro-2-naphthol",Oc2ccc1CCCCc1c2 ,0,0.72059132996633,-0.3066755995327425,0.72059132996633,0.8132534958427813,0.05436670683580329,13.473181818181827,12.373545454545452,13.462619545818182,58,0,0.29309232466607765,-0.507956035151618,0.507956035151618,0.29309232466607765,0.12396694214876032,0.1983471074380165,0.2727272727272727,3.1063858688217625,61.741305190372394,1.6519280360294821,1.5825680264058057,0.49165893549671486,0.9249778316735376,0.8056702214268904,0.26855674047563016,0.17764764956653922,0.17764764956653922,0.11155109751054235,0.11155109751054235,0.0694587776783613,0.0694587776783613,-0.0890909090909091,2.4035479460770888,0.13985483102745241,0.23378827342504707,1.2287392649941784,7.602353675990102,0.46443710828247387,0.5226828939349004,0.0,0.0,1.4311996572326342,0.0,0.0,0.0,0.0,0.0,0.549310791605754,4.427585454912554,0.9969153135783418,0.37384324259187823,1.8351956644526937,0.0,1.4311996572326342,0.0,0.0,2.317427236847047,0.0,29.254159106383874,0.0,5.749511833283905,0.0,0.0,5.749511833283905,0.0,0.5945461680308951,1.1587136184235236,0.0,3.541008809447744,18.127256122989884,0.0,0.0,0.0,1.839090909090909,5.499579140453208,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.1088081911072125,1.5008676159186414,8.444952683725006,0.0,0.0,0.3685456177420463,-0.1650420875420875,-0.07393213598570739,-0.2489531797567511,-1.1892072618188692,0.0,0.0,0.4,11,1,1,1,0,1,1,0,1,1,1,1,1,0,0,0,2,2.271,4.068072727272729,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+Methoxychlor,COc1ccc(cc1)C(c2ccc(OC)cc2)C(Cl)(Cl)Cl,0,0.42818275789151644,-0.1517278439153439,0.42818275789151644,1.0125949385978246,0.03373951994713745,16.45966666666665,15.739666666666661,16.38160775047621,112,0,0.20085905887536595,-0.4967682126013862,0.4967682126013862,0.20085905887536595,0.04308390022675737,0.06122448979591837,0.07482993197278912,3.0753203420383146,50.581906489690006,1.349638894888029,1.1881138095216859,0.5818179446671797,0.760464561181948,0.6015925376400746,0.2984446052128214,0.16595864308278235,0.27458875850180886,0.10436769739146587,0.13136515974926785,0.0648855952375644,0.09188305759536634,-0.0519047619047619,2.6919205278343616,0.262908249001727,0.32287214559569233,3.9690873213681592,7.626652972634444,0.4511298051238142,0.547572555550848,0.0,0.18059694931094147,0.0,0.0,0.0,0.0,0.0,0.0,2.8082140238738442,1.6807894197165318,0.06527421696048667,1.8647848756910168,1.430243059531114,1.6572771271760736,0.0,0.0,0.0,0.46126160099489766,14.075904917765177,59.46625264470035,0.0,11.49902366656781,0.4511298051238142,0.0,11.49902366656781,34.802819670697545,0.8508781358711881,0.0,0.0,1.789630429110018,48.33934966130636,0.0,0.0,0.0,0.8790476190476191,4.5108416595202945,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,9.473725907600098,2.636390381583376,5.895371728524128,0.8602657475394624,0.0,0.0,-0.09914624425607982,-0.24816545711513674,-0.42295152350201126,0.0,0.0,-0.30283456865068054,0.25,21,0,2,0,0,0,2,0,2,2,0,5,6,0,0,0,2,5.205900000000002,4.196238095238097,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,3,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+Flutriafol,OC(Cn1cncn1)(c2ccc(F)cc2)c3ccccc3F,0,0.7048295239813097,-0.16795153920153935,0.7048295239813097,0.027704554043839646,0.036603047711008606,13.695272727272716,13.099636363636364,13.686484930727289,112,0,0.21195469277087936,-0.3783120463266,0.3783120463266,0.21195469277087936,0.05578512396694215,0.09090909090909091,0.12190082644628099,2.71677024616709,56.17892754512513,1.2173442900638198,1.0684462737719178,0.4775371828628268,0.730509786410389,0.5481077680661326,0.2568237548632297,0.1770254948855497,0.1770254948855497,0.11559742294245176,0.11559742294245176,0.0730995469752152,0.0730995469752152,-0.11818181818181815,3.156940943735455,0.2522887064260781,0.2568232530178249,2.6031861872552517,6.526315841667404,0.23221855414123693,1.481079683334017,0.0,0.06505452987421065,0.0,0.0,0.0,18.446611551442032,5.098681808301038,0.0,1.373276979014385,1.0768503461221308,0.2528841587134998,0.9183838748190065,1.379033680022704,0.0,1.4311996572326342,0.6711119665633377,0.0,0.5499050225136197,0.0,83.7074377547246,0.0,0.0,0.0,8.78083009534964,0.0,0.0,0.9683850505787853,0.5499050225136197,11.63444168209179,1.253454802610756,60.946093089238815,0.0,0.0,0.0,2.3154545454545454,4.354788556819284,0.19956432034885543,0.19956432034885543,4.681802935145185,0.0,0.0,0.0,0.0,0.0,15.191468520355459,0.812741015057967,6.067314454541637,0.0,0.1480243382425922,0.3578803697404887,-0.29510732323232325,-0.16287614189895938,-0.47535187475663676,-0.09314440464738087,-0.16795153920153935,0.0,0.125,22,1,4,0,0,0,2,1,3,4,1,6,5,0,0,0,3,2.492400000000001,3.4464000000000015,0,1,0,0,0,3,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+Pyridine,c1ccncc1,0,1.1784567901234568,-0.07006172839506171,1.1784567901234568,0.3972222222222219,0.07552466091404841,13.183666666666662,12.343666666666666,13.17369986,30,0,0.08397776710017825,-0.264736970888988,0.264736970888988,0.08397776710017825,0.19444444444444445,0.3055555555555555,0.4166666666666667,3.4555464838177836,45.194086379598126,1.4323096878557795,1.324535599249993,0.49120226591665966,0.8394302100348646,0.6578689325833263,0.24120226591665964,0.11840169943749473,0.11840169943749473,0.058101132958329825,0.058101132958329825,0.028500708098956138,0.028500708098956138,-0.1416666666666667,1.9718983443825908,0.14348948854214696,0.25876378546517115,0.5490008483363183,7.279317266880425,0.0,0.0,0.0,0.0,0.0,0.0,0.8306630868245347,0.0,0.0,0.0,1.0070697846105492,2.0141395692210984,2.0576317369382537,1.142298796808517,1.142298796808517,0.0,0.0,0.8306630868245347,0.0,0.0,0.0,30.473046544619407,0.0,0.0,0.0,0.0,0.0,0.0,0.8306630868245347,0.0,0.0,1.142298796808517,30.473046544619407,0.0,0.0,0.0,2.1483333333333334,5.0788410907699015,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.9839785209472085,1.142298796808517,5.850679012345679,0.0,0.5453086419753087,0.0,0.0,0.0,-0.2003086419753086,-0.14012345679012342,0.0,0.0,0.0,6,0,1,0,0,0,0,1,1,1,0,1,0,0,0,0,1,1.0816,4.039499999999999,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0
+"1,1,2,2-Tetrachloroethane",ClC(Cl)C(Cl)Cl,0,1.1104681069958848,-0.3617541152263373,1.1104681069958848,2.170524691358024,0.0878852812624548,27.975000000000005,27.639,27.648510130666665,38,0,0.13734361253139798,-0.10236499019348498,0.13734361253139798,0.10236499019348498,0.16666666666666666,0.19444444444444445,0.19444444444444445,4.020391543176799,11.169925001442314,1.1666666666666667,0.7519763153394848,0.9225956126851211,0.5416666666666666,0.3343214910030758,0.4196311396758939,0.08680360264439502,0.4032679507903279,0.023809523809523805,0.2142857142857143,0.0,0.0,0.19333333333333333,1.253297578463043,0.6800531846725618,0.5332155675540714,3.3537984496124036,9.854533175014748,0.0,1.6041071098172466,0.0,0.0,0.0,0.0,0.0,0.0,0.0,46.40375956093006,0.0,0.0,0.0,0.45691951872340675,0.45691951872340675,7.733959926821677,0.0,0.0,0.0,1.6041071098172466,0.0,0.0,0.0,0.0,0.0,0.0,0.0,46.40375956093006,1.6041071098172466,0.0,0.0,0.45691951872340675,0.0,0.0,0.0,0.0,0.0,1.6041071098172466,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,8.190879445545084,2.2209362139917697,3.326646090534979,-0.7235082304526746,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,6,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,2.5938,5.164666666666666,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+"1,1-Dichloroethane",CC(Cl)Cl,0,1.6379243827160495,-0.6537422839506172,1.6379243827160495,2.3680555555555554,0.10149572252787546,24.739999999999995,23.732,24.492251372,26,0,0.10458917262570656,-0.10570628923282722,0.10570628923282722,0.10458917262570656,0.5625,0.625,0.625,4.020391543176799,16.75488750216347,1.75,1.4389822365046137,0.8169467095138407,0.8125,0.6569911182523068,0.3459733547569204,0.06510270198329625,0.30245096309274594,0.0,0.0,0.0,0.0,0.145,1.253297578463043,0.25540719571710024,0.4648324022346369,19.145992865636153,10.332651899165192,0.0,1.2030803323629349,0.0,0.0,0.0,0.0,0.0,0.0,0.0,23.20187978046503,0.0,1.7129730293239198,1.0280689171276651,0.34268963904255506,1.3707585561702202,5.800469945116258,0.0,0.0,0.0,2.9160533616868545,0.0,0.0,0.0,0.0,0.0,0.0,0.0,23.20187978046503,1.2030803323629349,0.0,0.0,3.0837315854941396,0.0,0.0,0.0,0.0,0.0,2.9160533616868545,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,7.171228501286477,6.486882716049383,2.4394290123456788,-0.5920138888888888,0.0,0.0,0.0,0.0,0.0,-0.6537422839506172,0.0,1.0,4,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,1.81,5.291999999999998,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+"2,4-Dimethyl-3-pentanol",CC(C)C(O)C(C)C,0,0.984375,-0.5390625,0.984375,3.675833333333333,0.07267607723915709,14.525499999999989,12.509499999999997,14.5150143915,50,0,0.21047279161185978,-0.39259643422498974,0.39259643422498974,0.21047279161185978,0.171875,0.234375,0.265625,7.1718964394277505,47.87591055624715,2.5258883476483183,2.488531036307983,0.4885310363079829,1.2575825214724778,1.2015465544619743,0.21301551815399145,0.13489051815399145,0.13489051815399145,0.056765518153991446,0.056765518153991446,0.015625,0.015625,-0.005,2.2946109309646077,0.09143191006325195,0.38494240274234687,2.3141072195146584,9.28616696590218,0.6386010238884016,0.0,0.0,0.17889995715407928,0.0,0.0,0.0,0.0,0.0,0.0,3.4259460586478396,1.4734894213407697,2.3988274732978847,0.931347072890026,3.2087733167075636,0.0,1.4311996572326342,0.0,11.787915370726157,4.185948312016588,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.5775032344112294,0.0,11.787915370726157,5.996118351467007,0.0,0.0,0.0,0.0,2.52875,5.659437733357357,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.1088081911072125,2.7490722499732416,14.657708333333328,0.0,0.0,0.4594791666666666,0.0,-1.055729166666667,0.0,-0.5390625,-1.991145833333334,0.0,1.0,8,1,1,0,0,0,0,0,0,1,1,1,7,0,0,0,0,1.6593,4.460349999999999,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+O-Ethyl carbamate,CCOC(=O)N,0,1.740636574074074,-0.5316743827160494,1.740636574074074,0.5520138888888892,0.08250323239115749,14.848999999999995,13.673,14.841279744,36,0,0.40397583424515676,-0.4500081277705684,0.4500081277705684,0.40397583424515676,0.4444444444444444,0.5833333333333334,0.6666666666666666,4.435198622708063,33.06742676583027,1.8103012199276332,1.6272850294046142,0.4606183627379474,0.9295309703265578,0.7467344040742817,0.18099653890762893,0.0783386107333867,0.0783386107333867,0.023056874905568903,0.023056874905568903,0.007275851093785877,0.007275851093785877,-0.12166666666666666,1.8275819057038027,0.1260861206486375,0.4173653395784543,3.2699999999999982,7.6725000081395835,1.7427633215877203,0.0,0.47061402610940023,0.0,0.0,6.093240070938415,0.0,4.794537184071822,0.0,0.0,0.0,1.1419820195492798,0.6853792780851101,1.5500837258761215,3.201479512563228,1.015540011823069,0.0,0.0,5.719716975726273,1.1419820195492798,6.558985242916289,0.0,0.0,0.0,0.9532861626210455,4.794537184071822,0.0,0.0,2.108704218975784,0.7894771589666748,0.0,2.2842808163577963,0.0,2.8236841566564013,0.0,0.0,8.72,4.2039724011461095,0.799089530678637,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.736862953800049,1.6129128229179173,8.299753086419752,0.0,1.740636574074074,0.0,-0.09200231481481487,0.0,0.0,-0.2822337962962963,-1.055042438271605,0.0,0.6666666666666666,6,2,3,0,0,0,0,0,0,2,1,3,2,0,0,0,0,0.10160000000000013,3.5349,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0
+"2-Methyl-1,3-Butadiene ",CC(=C)C=C,0,1.4033564814814814,-0.5576851851851853,1.4033564814814814,0.8680555555555551,0.082102936447801,13.623799999999997,12.011,13.612520051199999,28,0,0.06190242168807648,-0.09882310718358248,0.09882310718358248,0.06190242168807648,0.52,0.72,0.76,5.369075525460887,49.639635298459254,2.1618802153517005,2.1,0.5,1.1350852961085884,1.0,0.2,0.1,0.1,0.025,0.025,0.0,0.0,-0.10400000000000001,1.8176188699962486,0.08233466474037567,0.3534712643678163,3.4800000000000026,8.811915484478,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.937450840021714,1.3703784234591359,0.8224551337021321,1.3707585561702202,2.193213689872352,0.0,0.0,0.0,0.0,1.3703784234591359,0.0,24.68725420010857,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.563592113331487,24.68725420010857,0.0,0.0,0.0,0.0,6.3078292634808495,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.193213689872352,10.908124999999998,0.0,0.0,0.0,-0.17361111111111102,0.0,-0.1805555555555555,0.0,-0.9706250000000001,0.0,0.2,5,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1.7485,5.0021999999999975,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
diff --git a/atomsci/ddm/test/integrative/import_transformer/test_generate_transformers.py b/atomsci/ddm/test/integrative/import_transformer/test_generate_transformers.py
new file mode 100644
index 00000000..887a61b5
--- /dev/null
+++ b/atomsci/ddm/test/integrative/import_transformer/test_generate_transformers.py
@@ -0,0 +1,199 @@
+import atomsci.ddm.utils.generate_transformers as gt
+import atomsci.ddm.pipeline.parameter_parser as pp
+import atomsci.ddm.pipeline.model_pipeline as mp
+import atomsci.ddm.pipeline.transformations as trans
+import atomsci.ddm.pipeline.compare_models as cm
+import atomsci.ddm.utils.model_file_reader as mfr
+from sklearn.preprocessing import PowerTransformer
+from sklearn.pipeline import Pipeline
+
+import os
+import shutil
+import pandas as pd
+import pickle as pkl
+import tempfile
+import pytest
+
+def rel_path(filename):
+    """
+    Returns the absolute path to `filename` relative to the location of this file.
+    """
+    return os.path.join(os.path.dirname(os.path.abspath(__file__)), filename)
+
+def clean(temp_dir, transformers_pkl_path):
+    if os.path.exists(temp_dir):
+        shutil.rmtree(temp_dir)
+
+    if os.path.exists(transformers_pkl_path):
+        os.remove(transformers_pkl_path)
+
+def test_transformer_generation():
+    descriptor_type = 'rdkit_scaled'
+    temp_root = rel_path('temp')
+    transformers_pkl_path = rel_path('feature_transformers.pkl')
+    clean(temp_root, transformers_pkl_path)
+    assert not os.path.exists(temp_root)
+    os.makedirs(temp_root, exist_ok=True)
+
+    prepared_H1 = gt.prepare_csv_and_descriptor_with_dummy_response(
+        rel_path('data/H1_std_short.csv'),
+        descriptor_type=descriptor_type,
+        temp_root=temp_root,
+        split_uuid='test-split'
+    )
+
+    assert os.path.exists(temp_root)
+    assert os.path.exists(os.path.join(temp_root, 'H1_std_short.csv'))
+    assert os.path.exists(os.path.join(temp_root, 'H1_std_short_train_valid_test_scaffold_test-split.csv'))
+    assert 'dummy_response' in pd.read_csv(os.path.join(temp_root, 'H1_std_short.csv')).columns
+
+    H1_config = {
+                'dataset_key':prepared_H1,
+                'previously_split':'True',
+                'id_col':'compound_id',
+                'smiles_col':'base_rdkit_smiles',
+                'response_cols':'dummy_response',
+                'split_uuid':'test-split',
+                'splitter':'scaffold',
+                }
+
+    prepared_delaney = gt.prepare_csv_and_descriptor_with_dummy_response(
+        rel_path('data/delaney-processed_curated_fit_short.csv'),
+        descriptor_type=descriptor_type,
+        temp_root=temp_root
+    )
+
+    assert os.path.exists(os.path.join(temp_root, 'scaled_descriptors/delaney-processed_curated_fit_short_with_rdkit_scaled_descriptors.csv'))
+
+    delaney_config = {
+                'dataset_key': prepared_delaney,
+                'id_col':'Compound ID',
+                'smiles_col':'smiles',
+                'response_cols':'dummy_response',
+            }
+
+    aurk_config = {
+                'dataset_key': rel_path('data/aurka_chembl_base_smiles_union_short.csv'),
+                'id_col':'compound_id',
+                'smiles_col':'base_rdkit_smiles',
+                'response_cols':'pIC50',
+            }
+
+    transformer_dataset_key_configs = [H1_config, delaney_config, aurk_config]
+    combined_dataset = gt.load_all_datasets(transformer_dataset_key_configs,
+                                            featurizer='computed_descriptors',
+                                            descriptor_type=descriptor_type)
+
+    # aurka and delaney should have 200 each. H1 uses only 200 compounds from the training set
+    # some compounds fail to featurize. There should be less than 600 compouds total
+    assert len(combined_dataset) < 600
+
+    # fit transformers
+    gt.build_and_save_feature_transformers_from_csvs(
+        transformer_dataset_key_configs,
+        dest_pkl_path=transformers_pkl_path,
+        featurizer='computed_descriptors',
+        descriptor_type=descriptor_type,
+        feature_transform_type='PowerTransformer',
+        powertransformer_standardize='False',
+    )
+
+    with open(transformers_pkl_path, 'rb') as transformers_pkl_file:
+        saved_transformers = pkl.load(transformers_pkl_file)
+
+    assert 'transformers_x' in saved_transformers
+    assert 'params' in saved_transformers
+
+    assert transformer_dataset_key_configs == saved_transformers['params']['transformer_dataset_key_configs']
+    assert saved_transformers['params']['featurizer'] == 'computed_descriptors'
+    assert saved_transformers['params']['descriptor_type'] == descriptor_type
+    assert saved_transformers['params']['feature_transform_type'] == 'PowerTransformer'
+
+    # test load transformers
+    # first create a params json this uses a RobustScaler, we will check
+    # that the PowerTransformer is used instead
+    params_json = {
+        "dataset_key" : rel_path('data/H1_std_short.csv'),
+        "datastore" : "False",
+        "uncertainty": "False",
+        "splitter": "scaffold",
+        "previously_split": "True",
+        "split_uuid": "test-split",
+        "prediction_type": "regression",
+        "response_cols" : "pKi_mean",
+        "id_col": "compound_id",
+        "smiles_col" : "base_rdkit_smiles",
+        "result_dir": temp_root,
+        "model_type": "RF",
+        "featurizer": "ecfp",
+        "descriptor_type": 'rdkit_raw',
+        "feature_transform_type": "RobustScaler",
+        "feature_transform_path": transformers_pkl_path,
+        "save_results": "False",
+        "verbose": "False",
+        "seed":"0"
+    }
+
+    # this one will fail because it has the wrong featurizer
+    with pytest.raises(ValueError) as excinfo:
+        bad_params1 = pp.wrapper(params_json)
+        model_pipeline = mp.ModelPipeline(bad_params1)
+        model_pipeline.train_model()
+    assert 'Loaded transformers do not match featurizer.' in str(excinfo.value)
+
+    # This one will fail because it ahas the wrong descriptor type
+    params_json['featurizer'] = 'computed_descriptors'
+    with pytest.raises(ValueError) as excinfo:
+        bad_params2 = pp.wrapper(params_json)
+        model_pipeline = mp.ModelPipeline(bad_params2)
+        model_pipeline.train_model()
+    assert 'Loaded transformers do not match descriptor_type.' in str(excinfo.value)
+
+    # This one will succeed.
+    params_json['descriptor_type'] = descriptor_type
+    test_params = pp.wrapper(params_json)
+    model_pipeline = mp.ModelPipeline(test_params)
+    model_pipeline.train_model()
+
+    # check that the PowerTransformer was used instead of RobustScaler
+    assert model_pipeline.params.feature_transform_type == 'PowerTransformer'
+    transformers_x = model_pipeline.model_wrapper.transformers_x
+    assert len(transformers_x[0])==1
+    assert isinstance(transformers_x[0][0], trans.SklearnPipelineWrapper)
+    assert isinstance(transformers_x[0][0].sklearn_pipeline, Pipeline)
+    scaler = transformers_x[0][0].sklearn_pipeline.named_steps['PowerTransformer']
+    assert isinstance(scaler, PowerTransformer)
+
+    # check that the saved transformer pkl is a PowerTransformer
+    # reload the pipeline and see that it was loaded correctly works
+    results = cm.get_filesystem_perf_results(temp_root, pred_type='regression')
+    model_path = results['model_path'].values[0]
+    pred_params = {
+        'featurizer': 'computed_descriptors',
+        'result_dir': tempfile.mkdtemp(),
+        'id_col': 'compound_id',
+        'smiles_col':'base_rdkit_smiles' 
+    }
+    pred_params = pp.wrapper(pred_params)
+    reloaded_pipeline = mp.create_prediction_pipeline_from_file(pred_params, reload_dir=None, model_path=model_path)
+
+    assert reloaded_pipeline.params.feature_transform_type == 'PowerTransformer'
+    rl_formers_x = reloaded_pipeline.model_wrapper.transformers_x
+    scaler = rl_formers_x[0][0].sklearn_pipeline.named_steps['PowerTransformer']
+    assert isinstance(scaler, PowerTransformer)
+
+    # check that model metadata shows that this was a PowerTransformer
+    # check that standardize is set to false
+    reader = mfr.ModelFileReader(model_path)
+    assert not reader.get_powertransformer_standardize()
+
+    # check that the dataset_key configs were also saved in the parameters
+    loaded_trans_dskey_configs = reader.get_transformer_dataset_key_configs()
+    assert loaded_trans_dskey_configs == transformer_dataset_key_configs
+
+
+    # cleanup
+    clean(temp_root, transformers_pkl_path)
+
+if __name__ == '__main__':
+    test_transformer_generation()
\ No newline at end of file
diff --git a/atomsci/ddm/test/integrative/integrative_batch_chunk_tests b/atomsci/ddm/test/integrative/integrative_batch_chunk_tests
index cceb7ba5..18911cad 100755
--- a/atomsci/ddm/test/integrative/integrative_batch_chunk_tests
+++ b/atomsci/ddm/test/integrative/integrative_batch_chunk_tests
@@ -41,7 +41,7 @@ for folder in "${folders[@]}"; do
         if [[ -f "$file" && "$folder" != "__pycache__/" ]]; then
             cd "$folder"
             echo "Testing $folder"
-            pytest --capture=sys --capture=fd --cov=atomsci -vv
+            pytest -m "not moe_required" --capture=fd --cov=atomsci -vv --durations=8 --durations-min=0.05
             cd ..
         else
           echo "Skipping folder: $folder. Not a test directory."
diff --git a/atomsci/ddm/test/integrative/shortlist_test/test_shortlist.py b/atomsci/ddm/test/integrative/shortlist_test/test_shortlist.py
index d78c8eb0..17cc8b28 100644
--- a/atomsci/ddm/test/integrative/shortlist_test/test_shortlist.py
+++ b/atomsci/ddm/test/integrative/shortlist_test/test_shortlist.py
@@ -6,6 +6,7 @@
 import os
 import time
 import pandas as pd
+from contextlib import contextmanager
 
 import tempfile
 import tarfile
@@ -16,6 +17,19 @@
 from atomsci.ddm.utils import llnl_utils
 import atomsci.ddm.utils.file_utils as futils
 
+
+TEST_DIR = os.path.dirname(os.path.realpath(__file__))
+
+
+@contextmanager
+def _in_test_dir():
+    prev_cwd = os.getcwd()
+    os.chdir(TEST_DIR)
+    try:
+        yield
+    finally:
+        os.chdir(prev_cwd)
+
 def init_data():
     """Copy files necessary for running tests"""
     if not os.path.exists('data'):
@@ -171,42 +185,41 @@ def wait_to_finish(split_json, search_json, max_time=1200):
 def test():
     """Test full model pipeline: Split data, featurize data, fit model, get results"""
 
-    # Clean
-    # -----
-    clean()
+    with _in_test_dir():
+        # Clean
+        # -----
+        clean()
 
-    # Init Data
-    # -----
-    init_data()
+        # Init Data
+        # -----
+        init_data()
 
-    # Run shortlist hyperparam search
-    # ------------
-    if llnl_utils.is_lc_system():
-        result_df = wait_to_finish("test_shortlist_split_config.json",
-            "test_shortlist_RF-NN-XG_hyperconfig.json", max_time=-1)
-        assert len(result_df) == 18 # Timed out
-    else:
-        assert True
+        # Run shortlist hyperparam search
+        # ------------
+        if llnl_utils.is_lc_system():
+            result_df = wait_to_finish("test_shortlist_split_config.json",
+                "test_shortlist_RF-NN-XG_hyperconfig.json", max_time=-1)
+            assert len(result_df) == 18 # Timed out
+        else:
+            assert True
 
-    # Clean
-    # -----
-    clean()
+        # Clean
+        # -----
+        clean()
 
 def extract_split_uuid(tar_file):
     """Given a tar file, return split uuid used to train the model."""
+    with tempfile.TemporaryDirectory() as tmpdir:
+        with tarfile.open(tar_file, mode='r:gz') as tar:
+            futils.safe_extract(tar, path=tmpdir)
 
-    tmpdir = tempfile.mkdtemp()
-
-    with tarfile.open(tar_file, mode='r:gz') as tar:
-        futils.safe_extract(tar, path=tmpdir)
-
-    # make metadata path
-    metadata_path = os.path.join(tmpdir, 'model_metadata.json')
+        # make metadata path
+        metadata_path = os.path.join(tmpdir, 'model_metadata.json')
 
-    with open(metadata_path, 'r') as json_file:
-        json_dat = json.load(json_file)
+        with open(metadata_path, 'r') as json_file:
+            json_dat = json.load(json_file)
 
-    split_uuid = json_dat['splitting_parameters']['split_uuid']
+        split_uuid = json_dat['splitting_parameters']['split_uuid']
 
     return split_uuid
 
diff --git a/atomsci/ddm/test/integrative/wenzel_NN/config_wenzel_fit_NN.json b/atomsci/ddm/test/integrative/wenzel_NN/config_wenzel_fit_NN.json
index a1100018..6859fef1 100644
--- a/atomsci/ddm/test/integrative/wenzel_NN/config_wenzel_fit_NN.json
+++ b/atomsci/ddm/test/integrative/wenzel_NN/config_wenzel_fit_NN.json
@@ -35,6 +35,8 @@
 
     "comment": "Results",
     "comment": "----------------------------------------",    
-    "result_dir": "result"
+    "result_dir": "result",
+
+    "seed":0
 }
 
diff --git a/atomsci/ddm/test/integrative/wenzel_NN/test_wenzel_NN.py b/atomsci/ddm/test/integrative/wenzel_NN/test_wenzel_NN.py
index ff04ecb5..b7975ada 100644
--- a/atomsci/ddm/test/integrative/wenzel_NN/test_wenzel_NN.py
+++ b/atomsci/ddm/test/integrative/wenzel_NN/test_wenzel_NN.py
@@ -7,6 +7,7 @@
 import shutil
 import sys
 import tarfile
+from contextlib import contextmanager
 
 import atomsci.ddm.pipeline.model_pipeline as mp
 import atomsci.ddm.pipeline.parameter_parser as parse
@@ -19,6 +20,19 @@
 sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
 import integrative_utilities
 
+
+TEST_DIR = os.path.dirname(os.path.realpath(__file__))
+
+
+@contextmanager
+def _in_test_dir():
+    prev_cwd = os.getcwd()
+    os.chdir(TEST_DIR)
+    try:
+        yield
+    finally:
+        os.chdir(prev_cwd)
+
 def clean():
     """Clean test files"""
     for f in ['hlm_clearance_curated_predict.csv',
@@ -86,78 +100,80 @@ def check_for_data_zip():
 
 def test():
     """Test full model pipeline: Curate data, fit model, and predict property for new compounds"""
-    # Clean
-    # -----
-    integrative_utilities.clean_fit_predict()
-    clean()
-
-    # Check for data
-    # --------
-    check_for_data_zip()
-
-    # Curate
-    # ------
-    curate()
-
-    # Train model
-    # -----------
-    # Read parameter JSON file
-    with open('config_wenzel_fit_NN.json') as f:
-        config = json.loads(f.read())
-
-    # Parse parameters
-    params = parse.wrapper(config)
-
-    # Create model pipeline
-    model = mp.ModelPipeline(params)
-
-    # Train model
-    model.train_model()
-
-    # Get uuid and reload directory
-    # -----------------------------
-    uuid = integrative_utilities.get_subdirectory('result/hlm_clearance_curated_fit/NN_computed_descriptors_scaffold_regression')
-    reload_dir = 'result/hlm_clearance_curated_fit/NN_computed_descriptors_scaffold_regression/'+uuid
-
-    # Check training statistics
-    # -------------------------
-    integrative_utilities.training_statistics_file(reload_dir, 'valid', 0.1)
-
-    # Make prediction using the trained model
-    # -------------------------
-    result_df = cm.get_filesystem_perf_results('result', pred_type='regression')
-
-    # There should only be one model trained
-    # -------------------------
-    assert len(result_df) == 1
-    model_path = result_df.model_path[0] # this is the path to a tar file
-
-    # Load second test set
-    # --------------------
-    data = pd.read_csv('hlm_clearance_curated_external.csv')
-    # Make prediction pipeline
-    # ------------------------
-    predict = pfm.predict_from_model_file(model_path, data,
-                                id_col=params.id_col,
-                                smiles_col=params.smiles_col,
-                                response_col=params.response_cols,
-                                is_featurized=False)
-
-    # Check predictions
-    # -----------------
-    assert (predict['VALUE_NUM_mean_pred'].shape[0] == 348), 'Error: Incorrect number of predictions'
-    assert (np.all(np.isfinite(predict['VALUE_NUM_mean_pred'].values))), 'Error: Predictions are not numbers'
-
-    # Save predictions with experimental values
-    # -----------------------------------------
-    predict.reset_index(level=0, inplace=True)
-    combined = pd.merge(data, predict, on=params.id_col, how='inner')
-    combined.to_csv('hlm_clearance_curated_predict.csv')
-    assert (os.path.isfile('hlm_clearance_curated_predict.csv')
-            and os.path.getsize('hlm_clearance_curated_predict.csv') > 0), 'Error: Prediction file not created'
-
-    clean()
-    integrative_utilities.clean_fit_predict()
+    with _in_test_dir():
+        # Clean
+        # -----
+        integrative_utilities.clean_fit_predict()
+        clean()
+
+        try:
+            # Check for data
+            # --------
+            check_for_data_zip()
+
+            # Curate
+            # ------
+            curate()
+
+            # Train model
+            # -----------
+            # Read parameter JSON file
+            with open('config_wenzel_fit_NN.json') as f:
+                config = json.loads(f.read())
+
+            # Parse parameters
+            params = parse.wrapper(config)
+
+            # Create model pipeline
+            model = mp.ModelPipeline(params)
+
+            # Train model
+            model.train_model()
+
+            # Get uuid and reload directory
+            # -----------------------------
+            uuid = integrative_utilities.get_subdirectory('result/hlm_clearance_curated_fit/NN_computed_descriptors_scaffold_regression')
+            reload_dir = 'result/hlm_clearance_curated_fit/NN_computed_descriptors_scaffold_regression/'+uuid
+
+            # Check training statistics
+            # -------------------------
+            integrative_utilities.training_statistics_file(reload_dir, 'valid', 0.1)
+
+            # Make prediction using the trained model
+            # -------------------------
+            result_df = cm.get_filesystem_perf_results('result', pred_type='regression')
+
+            # There should only be one model trained
+            # -------------------------
+            assert len(result_df) == 1
+            model_path = result_df.model_path[0] # this is the path to a tar file
+
+            # Load second test set
+            # --------------------
+            data = pd.read_csv('hlm_clearance_curated_external.csv')
+            # Make prediction pipeline
+            # ------------------------
+            predict = pfm.predict_from_model_file(model_path, data,
+                                        id_col=params.id_col,
+                                        smiles_col=params.smiles_col,
+                                        response_col=params.response_cols,
+                                        is_featurized=False)
+
+            # Check predictions
+            # -----------------
+            assert (predict['VALUE_NUM_mean_pred'].shape[0] == 348), 'Error: Incorrect number of predictions'
+            assert (np.all(np.isfinite(predict['VALUE_NUM_mean_pred'].values))), 'Error: Predictions are not numbers'
+
+            # Save predictions with experimental values
+            # -----------------------------------------
+            predict.reset_index(level=0, inplace=True)
+            combined = pd.merge(data, predict, on=params.id_col, how='inner')
+            combined.to_csv('hlm_clearance_curated_predict.csv')
+            assert (os.path.isfile('hlm_clearance_curated_predict.csv')
+                    and os.path.getsize('hlm_clearance_curated_predict.csv') > 0), 'Error: Prediction file not created'
+        finally:
+            clean()
+            integrative_utilities.clean_fit_predict()
 
 if __name__ == '__main__':
     test()
diff --git a/atomsci/ddm/test/test_datasets/Molport_test.csv b/atomsci/ddm/test/test_datasets/Molport_test.csv
new file mode 100644
index 00000000..d91d501c
--- /dev/null
+++ b/atomsci/ddm/test/test_datasets/Molport_test.csv
@@ -0,0 +1,101 @@
+compound_id,SMILES,base_rdkit_smiles,dummy_response
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\ No newline at end of file
diff --git a/atomsci/ddm/test/test_datasets/Molport_test_train_valid_test_random_e28e63fb-4d30-4cef-b303-7f0622f64688.csv b/atomsci/ddm/test/test_datasets/Molport_test_train_valid_test_random_e28e63fb-4d30-4cef-b303-7f0622f64688.csv
new file mode 100644
index 00000000..e1a1a456
--- /dev/null
+++ b/atomsci/ddm/test/test_datasets/Molport_test_train_valid_test_random_e28e63fb-4d30-4cef-b303-7f0622f64688.csv
@@ -0,0 +1,101 @@
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\ No newline at end of file
diff --git a/atomsci/ddm/test/test_datasets/Molport_test_train_valid_test_scaffold_e28e63fb-4d30-4cef-b303-7f0622f64688.csv b/atomsci/ddm/test/test_datasets/Molport_test_train_valid_test_scaffold_e28e63fb-4d30-4cef-b303-7f0622f64688.csv
new file mode 100644
index 00000000..ca3b982d
--- /dev/null
+++ b/atomsci/ddm/test/test_datasets/Molport_test_train_valid_test_scaffold_e28e63fb-4d30-4cef-b303-7f0622f64688.csv
@@ -0,0 +1,101 @@
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\ No newline at end of file
diff --git a/atomsci/ddm/test/test_datasets/pGP_MDCK_efflux_ratio_chembl29.csv b/atomsci/ddm/test/test_datasets/pGP_MDCK_efflux_ratio_chembl29.csv
index aa8ed72d..d9c87ed8 100644
--- a/atomsci/ddm/test/test_datasets/pGP_MDCK_efflux_ratio_chembl29.csv
+++ b/atomsci/ddm/test/test_datasets/pGP_MDCK_efflux_ratio_chembl29.csv
@@ -1099,3 +1099,4 @@ activity_id,compound_id,base_rdkit_smiles,relation,efflux_ratio,log_efflux_ratio
 20668378,CHEMBL3892073,NC(=O)c1cc(-c2ccc(F)cc2)c2ccc(CN3C(=O)CCC3=O)cc2n1,=,7.0,0.8450980400142568
 20679457,CHEMBL4644751,C[C@H](C(=O)Nc1ccc(F)cc1)C12CC(NC(=O)c3ccc(F)c(F)c3)(C1)C2,=,1.3,0.11394335230683678
 20717398,CHEMBL4643105,COc1cc(/C=C2\CCCN3C2=NO[C@H](c2cc(F)cc(F)c2)[C@@H]3C)ccc1-n1cnc(C)c1,=,2.46,0.39093510710337914
+20717398,CHEMBL4643105,COc1cc(/C=C2\CCCN3C2=NO[C@H](c2cc(F)cc(F)c2)[C@@H]3C)ccc1-n1cnc(C)c1,=,,
diff --git a/atomsci/ddm/test/test_datasets/scaled_descriptors/MRP3_dataset_with_mordred_filtered_descriptors.csv b/atomsci/ddm/test/test_datasets/scaled_descriptors/MRP3_dataset_with_mordred_filtered_descriptors.csv
new file mode 100644
index 00000000..f1529725
--- /dev/null
+++ b/atomsci/ddm/test/test_datasets/scaled_descriptors/MRP3_dataset_with_mordred_filtered_descriptors.csv
@@ -0,0 +1,729 @@
+compound_id,rdkit_smiles,active,AATS0Z,AATS0are,AATS0d,AATS0dv,AATS0i,AATS0m,AATS0p,AATS0pe,AATS0s,AATS0se,AATS0v,AATS1Z,AATS1are,AATS1d,AATS1dv,AATS1i,AATS1m,AATS1p,AATS1pe,AATS1s,AATS1se,AATS1v,AATS2Z,AATS2are,AATS2d,AATS2dv,AATS2i,AATS2m,AATS2p,AATS2pe,AATS2s,AATS2se,AATS2v,AATS3Z,AATS3are,AATS3d,AATS3dv,AATS3i,AATS3m,AATS3p,AATS3pe,AATS3s,AATS3se,AATS3v,AATS4Z,AATS4are,AATS4d,AATS4dv,AATS4i,AATS4m,AATS4p,AATS4pe,AATS4s,AATS4se,AATS4v,AATS5Z,AATS5are,AATS5d,AATS5dv,AATS5i,AATS5m,AATS5p,AATS5pe,AATS5s,AATS5se,AATS5v,AATS6Z,AATS6are,AATS6d,AATS6dv,AATS6i,AATS6m,AATS6p,AATS6pe,AATS6s,AATS6se,AATS6v,AATS7Z,AATS7are,AATS7d,AATS7dv,AATS7i,AATS7m,AATS7p,AATS7pe,AATS7s,AATS7se,AATS7v,AATS8Z,AATS8are,AATS8d,AATS8dv,AATS8i,AATS8m,AATS8p,AATS8pe,AATS8s,AATS8se,AATS8v,AATSC0Z,AATSC0are,AATSC0c,AATSC0d,AATSC0dv,AATSC0i,AATSC0m,AATSC0p,AATSC0pe,AATSC0s,AATSC0se,AATSC0v,AATSC1Z,AATSC1are,AATSC1c,AATSC1d,AATSC1dv,AATSC1i,AATSC1m,AATSC1p,AATSC1pe,AATSC1s,AATSC1se,AATSC1v,AATSC2Z,AATSC2are,AATSC2c,AATSC2d,AATSC2dv,AATSC2i,AATSC2m,AATSC2p,AATSC2pe,AATSC2s,AATSC2se,AATSC2v,AATSC3Z,AATSC3are,AATSC3c,AATSC3d,AATSC3dv,AATSC3i,AATSC3m,AATSC3p,AATSC3pe,AATSC3s,AATSC3se,AATSC3v,AATSC4Z,AATSC4are,AATSC4c,AATSC4d,AATSC4dv,AATSC4i,AATSC4m,AATSC4p,AATSC4pe,AATSC4s,AATSC4se,AATSC4v,AATSC5Z,AATSC5are,AATSC5c,AATSC5d,AATSC5dv,AATSC5i,AATSC5m,AATSC5p,AATSC5pe,AATSC5s,AATSC5se,AATSC5v,AATSC6Z,AATSC6are,AATSC6c,AATSC6d,AATSC6dv,AATSC6i,AATSC6m,AATSC6p,AATSC6pe,AATSC6s,AATSC6se,AATSC6v,AATSC7Z,AATSC7are,AATSC7c,AATSC7d,AATSC7dv,AATSC7i,AATSC7m,AATSC7p,AATSC7pe,AATSC7s,AATSC7se,AATSC7v,AATSC8Z,AATSC8are,AATSC8c,AATSC8d,AATSC8dv,AATSC8i,AATSC8m,AATSC8p,AATSC8pe,AATSC8s,AATSC8se,AATSC8v,ABC,ABCGG,AETA_alpha,AETA_beta,AETA_beta_ns,AETA_beta_ns_d,AETA_beta_s,AETA_dBeta,AETA_eta,AETA_eta_B,AETA_eta_BR,AETA_eta_F,AETA_eta_FL,AETA_eta_L,AETA_eta_R,AETA_eta_RL,AMID,AMID_C,AMID_N,AMID_O,AMID_X,AMID_h,AMW,ATS0Z,ATS0are,ATS0d,ATS0dv,ATS0i,ATS0m,ATS0p,ATS0pe,ATS0s,ATS0se,ATS0v,ATS1Z,ATS1are,ATS1d,ATS1dv,ATS1i,ATS1m,ATS1p,ATS1pe,ATS1s,ATS1se,ATS1v,ATS2Z,ATS2are,ATS2d,ATS2dv,ATS2i,ATS2m,ATS2p,ATS2pe,ATS2s,ATS2se,ATS2v,ATS3Z,ATS3are,ATS3d,ATS3dv,ATS3i,ATS3m,ATS3p,ATS3pe,ATS3s,ATS3se,ATS3v,ATS4Z,ATS4are,ATS4d,ATS4dv,ATS4i,ATS4m,ATS4p,ATS4pe,ATS4s,ATS4se,ATS4v,ATS5Z,ATS5are,ATS5d,ATS5dv,ATS5i,ATS5m,ATS5p,ATS5pe,ATS5s,ATS5se,ATS5v,ATS6Z,ATS6are,ATS6d,ATS6dv,ATS6i,ATS6m,ATS6p,ATS6pe,ATS6s,ATS6se,ATS6v,ATS7Z,ATS7are,ATS7d,ATS7dv,ATS7i,ATS7m,ATS7p,ATS7pe,ATS7s,ATS7se,ATS7v,ATS8Z,ATS8are,ATS8d,ATS8dv,ATS8i,ATS8m,ATS8p,ATS8pe,ATS8s,ATS8se,ATS8v,ATSC0Z,ATSC0are,ATSC0c,ATSC0d,ATSC0dv,ATSC0i,ATSC0m,ATSC0p,ATSC0pe,ATSC0s,ATSC0se,ATSC0v,ATSC1Z,ATSC1are,ATSC1c,ATSC1d,ATSC1dv,ATSC1i,ATSC1m,ATSC1p,ATSC1pe,ATSC1s,ATSC1se,ATSC1v,ATSC2Z,ATSC2are,ATSC2c,ATSC2d,ATSC2dv,ATSC2i,ATSC2m,ATSC2p,ATSC2pe,ATSC2s,ATSC2se,ATSC2v,ATSC3Z,ATSC3are,ATSC3c,ATSC3d,ATSC3dv,ATSC3i,ATSC3m,ATSC3p,ATSC3pe,ATSC3s,ATSC3se,ATSC3v,ATSC4Z,ATSC4are,ATSC4c,ATSC4d,ATSC4dv,ATSC4i,ATSC4m,ATSC4p,ATSC4pe,ATSC4s,ATSC4se,ATSC4v,ATSC5Z,ATSC5are,ATSC5c,ATSC5d,ATSC5dv,ATSC5i,ATSC5m,ATSC5p,ATSC5pe,ATSC5s,ATSC5se,ATSC5v,ATSC6Z,ATSC6are,ATSC6c,ATSC6d,ATSC6dv,ATSC6i,ATSC6m,ATSC6p,ATSC6pe,ATSC6s,ATSC6se,ATSC6v,ATSC7Z,ATSC7are,ATSC7c,ATSC7d,ATSC7dv,ATSC7i,ATSC7m,ATSC7p,ATSC7pe,ATSC7s,ATSC7se,ATSC7v,ATSC8Z,ATSC8are,ATSC8c,ATSC8d,ATSC8dv,ATSC8i,ATSC8m,ATSC8p,ATSC8pe,ATSC8s,ATSC8se,ATSC8v,AXp-0d,AXp-0dv,AXp-1d,AXp-1dv,AXp-2d,AXp-2dv,AXp-3d,AXp-3dv,AXp-4d,AXp-4dv,AXp-5d,AXp-5dv,AXp-6d,AXp-6dv,AXp-7d,AXp-7dv,BCUTZ-1h,BCUTZ-1l,BCUTare-1h,BCUTare-1l,BCUTc-1h,BCUTc-1l,BCUTd-1h,BCUTd-1l,BCUTdv-1h,BCUTdv-1l,BCUTi-1h,BCUTi-1l,BCUTm-1h,BCUTm-1l,BCUTp-1h,BCUTp-1l,BCUTpe-1h,BCUTpe-1l,BCUTs-1h,BCUTs-1l,BCUTse-1h,BCUTse-1l,BCUTv-1h,BCUTv-1l,BIC0,BIC1,BIC2,BIC3,BIC4,BIC5,BalabanJ,BertzCT,C1SP1,C1SP2,C1SP3,C2SP1,C2SP2,C2SP3,C3SP2,C3SP3,C4SP3,CIC0,CIC1,CIC2,CIC3,CIC4,CIC5,DPSA1,DPSA2,DPSA3,DPSA4,DPSA5,DetourIndex,Diameter,ECIndex,EState_VSA1,EState_VSA10,EState_VSA2,EState_VSA3,EState_VSA4,EState_VSA5,EState_VSA6,EState_VSA7,EState_VSA8,EState_VSA9,ETA_alpha,ETA_beta,ETA_beta_ns,ETA_beta_ns_d,ETA_beta_s,ETA_dAlpha_A,ETA_dAlpha_B,ETA_dBeta,ETA_dEpsilon_A,ETA_dEpsilon_B,ETA_dEpsilon_C,ETA_dEpsilon_D,ETA_dPsi_A,ETA_dPsi_B,ETA_epsilon_1,ETA_epsilon_2,ETA_epsilon_3,ETA_epsilon_4,ETA_epsilon_5,ETA_eta,ETA_eta_B,ETA_eta_BR,ETA_eta_F,ETA_eta_FL,ETA_eta_L,ETA_eta_R,ETA_eta_RL,ETA_psi_1,ETA_shape_p,ETA_shape_x,ETA_shape_y,FCSP3,FNSA1,FNSA2,FNSA3,FNSA4,FNSA5,FPSA1,FPSA2,FPSA3,FPSA4,FPSA5,FilterItLogS,GATS1Z,GATS1are,GATS1c,GATS1d,GATS1dv,GATS1i,GATS1m,GATS1p,GATS1pe,GATS1s,GATS1se,GATS1v,GATS2Z,GATS2are,GATS2c,GATS2d,GATS2dv,GATS2i,GATS2m,GATS2p,GATS2pe,GATS2s,GATS2se,GATS2v,GATS3Z,GATS3are,GATS3c,GATS3d,GATS3dv,GATS3i,GATS3m,GATS3p,GATS3pe,GATS3s,GATS3se,GATS3v,GATS4Z,GATS4are,GATS4c,GATS4d,GATS4dv,GATS4i,GATS4m,GATS4p,GATS4pe,GATS4s,GATS4se,GATS4v,GATS5Z,GATS5are,GATS5c,GATS5d,GATS5dv,GATS5i,GATS5m,GATS5p,GATS5pe,GATS5s,GATS5se,GATS5v,GATS6Z,GATS6are,GATS6c,GATS6d,GATS6dv,GATS6i,GATS6m,GATS6p,GATS6pe,GATS6s,GATS6se,GATS6v,GATS7Z,GATS7are,GATS7c,G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diff --git a/atomsci/ddm/test/test_datasets/scaled_descriptors/MRP3_dataset_with_rdkit_raw_descriptors.csv b/atomsci/ddm/test/test_datasets/scaled_descriptors/MRP3_dataset_with_rdkit_raw_descriptors.csv
new file mode 100644
index 00000000..f296d3d9
--- /dev/null
+++ b/atomsci/ddm/test/test_datasets/scaled_descriptors/MRP3_dataset_with_rdkit_raw_descriptors.csv
@@ -0,0 +1,729 @@
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diff --git a/atomsci/ddm/test/test_datasets/scaled_descriptors/Molport_test_with_rdkit_raw_descriptors.csv b/atomsci/ddm/test/test_datasets/scaled_descriptors/Molport_test_with_rdkit_raw_descriptors.csv
new file mode 100644
index 00000000..901a99f3
--- /dev/null
+++ b/atomsci/ddm/test/test_datasets/scaled_descriptors/Molport_test_with_rdkit_raw_descriptors.csv
@@ -0,0 +1,101 @@
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diff --git a/atomsci/ddm/test/unit/test_atomsci_init_version.py b/atomsci/ddm/test/unit/test_atomsci_init_version.py
new file mode 100644
index 00000000..f49e5a11
--- /dev/null
+++ b/atomsci/ddm/test/unit/test_atomsci_init_version.py
@@ -0,0 +1,35 @@
+import builtins
+import importlib
+
+import atomsci.ddm as ddm
+
+def test_ddm_init_version_package_not_found(monkeypatch):
+    md = importlib.import_module("importlib.metadata")
+
+    def fake_version(_name):
+        raise md.PackageNotFoundError("atomsci-ampl")
+
+    monkeypatch.setattr(md, "version", fake_version)
+
+    importlib.reload(ddm)
+    assert ddm.__version__ == "unknown"
+
+    # cleanup
+    importlib.reload(ddm)
+
+
+def test_ddm_init_version_no_metadata_modules(monkeypatch):
+    real_import = builtins.__import__
+
+    def blocked_import(name, globals=None, locals=None, fromlist=(), level=0):
+        if name in ("importlib.metadata", "importlib_metadata"):
+            raise ImportError
+        return real_import(name, globals, locals, fromlist, level)
+
+    monkeypatch.setattr(builtins, "__import__", blocked_import)
+
+    importlib.reload(ddm)
+    assert ddm.__version__ == "unknown"
+
+    # cleanup
+    importlib.reload(ddm)
\ No newline at end of file
diff --git a/atomsci/ddm/test/unit/test_compare_models.py b/atomsci/ddm/test/unit/test_compare_models.py
index f73c0fa3..15c8339a 100644
--- a/atomsci/ddm/test/unit/test_compare_models.py
+++ b/atomsci/ddm/test/unit/test_compare_models.py
@@ -7,6 +7,7 @@
 import os
 import json
 import tarfile
+import shutil
 import pytest
 import pandas as pd
 from glob import glob
@@ -20,20 +21,34 @@
 # Fixtures and Test Data
 # --------------------------
 
-@pytest.fixture
-def sample_result_dir():
-    """Fixture to use an existing sample result directory."""
+
+def _get_regression_dataset_key(sample_result_dir):
+    fs_df = compare_models.get_filesystem_perf_results(
+        str(sample_result_dir),
+        pred_type='regression',
+    )
+    assert not fs_df.empty, 'Expected regression fixture models to be present.'
+    return fs_df['dataset_key'].dropna().iloc[0]
+
+
+def _get_expected_model_count(sample_result_dir, dataset_key):
+    fs_df = compare_models.get_filesystem_perf_results(
+        str(sample_result_dir),
+        pred_type='regression',
+    )
+    filtered = fs_df[fs_df['dataset_key'] == dataset_key]
+    assert not filtered.empty, f'Expected regression fixture models for dataset_key={dataset_key}'
+    return filtered['model_uuid'].nunique()
+
+@pytest.fixture(scope="module")
+def unpack_tar_files_module_scope():
+    """Module-level fixture to create model directories with unpacked tar files"""
     # Resolve the path to the existing directory
     result_dir = Path(__file__).parent / "../../examples/tutorials/dataset/"
     assert result_dir.exists(), f"Directory {result_dir} does not exist."
-    return result_dir
-
-# @pytest.fixture
-def unpack_tar_files(sample_result_dir):
-    """Fixture to create a model directories with unpacked tar files"""
-
+    
     # get .tar.gz files
-    tar_list=glob(f'{sample_result_dir}/**/*.tar.gz', recursive=True)
+    tar_list=glob(f'{result_dir}/**/*.tar.gz', recursive=True)
 
     # unpack tar files into model directories
     delete_dirs = []
@@ -49,16 +64,21 @@ def unpack_tar_files(sample_result_dir):
         with tarfile.open(tar_file, mode='r:gz') as tar:
             futils.safe_extract(tar, path=extract_location)
 
-    return delete_dirs
+    yield delete_dirs
 
-# @pytest.fixture
-def delete_model_dirs(delete_dirs):
-    """Fixture to clean up model directories after tests"""
-    yield
-    # Cleanup
+    # Cleanup extracted trees even when they contain files.
     for dir in delete_dirs:
         if os.path.exists(dir):
-            os.rmdir(dir)
+            shutil.rmtree(dir, ignore_errors=True)
+
+
+@pytest.fixture
+def sample_result_dir(unpack_tar_files_module_scope):
+    """Fixture to use an existing sample result directory with unpacked tar files."""
+    # Resolve the path to the existing directory
+    result_dir = Path(__file__).parent / "../../examples/tutorials/dataset/"
+    assert result_dir.exists(), f"Directory {result_dir} does not exist."
+    return result_dir
 
 
 # --------------------------
@@ -68,26 +88,35 @@ def delete_model_dirs(delete_dirs):
 def test_basic_directory_processing(sample_result_dir):
     """Test basic JSON model discovery and processing"""
 
-    delete_dirs=unpack_tar_files(sample_result_dir)
+    dataset_key = _get_regression_dataset_key(sample_result_dir)
+    expected_models = _get_expected_model_count(sample_result_dir, dataset_key)
 
     df = compare_models.get_multitask_perf_from_files_new(
         str(sample_result_dir),
-        pred_type='regression'
+        pred_type='regression',
+        dataset_key=dataset_key,
     )
 
-    delete_model_dirs(delete_dirs)
-
-    assert df.model_uuid.nunique() == 5, f"Expected 5 unique models, but got {df.model_uuid.nunique()}"
+    assert df.model_uuid.nunique() == expected_models, (
+        f"Expected {expected_models} unique models, but got {df.model_uuid.nunique()}"
+    )
     assert all(df.prediction_type == 'regression'), "Not all rows have 'regression' as prediction_type"
 
 
 def test_tar_file_processing(sample_result_dir):
     """Test TAR archive handling"""
+    dataset_key = _get_regression_dataset_key(sample_result_dir)
+    expected_models = _get_expected_model_count(sample_result_dir, dataset_key)
+
     df = compare_models.get_multitask_perf_from_files_new(
         str(sample_result_dir),
+        pred_type='regression',
+        dataset_key=dataset_key,
         tar=True
     )
-    assert df.model_uuid.nunique() == 5, f"Expected 5 unique models, but got {df.model_uuid.nunique()}"
+    assert df.model_uuid.nunique() == expected_models, (
+        f"Expected {expected_models} unique models, but got {df.model_uuid.nunique()}"
+    )
     assert all(df.model_path.str.endswith('.tar.gz')), "Not all model paths end with '.tar.gz'"
     
 
@@ -115,7 +144,12 @@ def test_tar_file_processing(sample_result_dir):
 
 def test_dataframe_structure(sample_result_dir):
     """Validate DataFrame schema and data types"""
-    df = compare_models.get_multitask_perf_from_files_new(str(sample_result_dir))
+    dataset_key = _get_regression_dataset_key(sample_result_dir)
+    df = compare_models.get_multitask_perf_from_files_new(
+        str(sample_result_dir),
+        pred_type='regression',
+        dataset_key=dataset_key,
+    )
     
     # Required columns
     assert {'model_uuid', 'time_built', 'ampl_version','dataset_key', 'model_path',
@@ -152,14 +186,154 @@ def test_dataframe_structure(sample_result_dir):
 
 def test_mixed_model_types(sample_result_dir):
     """Test handling directories with both MT and ST model files"""
-    
+    dataset_key = _get_regression_dataset_key(sample_result_dir)
+
     df = compare_models.get_multitask_perf_from_files_new(
         str(sample_result_dir),
+        pred_type='regression',
+        dataset_key=dataset_key,
         tar=True
     )
-    
-    assert len(df.multitask.unique()) > 1, "Expected multiple multitask types"
-    assert df.multitask.max() == 1, "Expected at least one multitask model"
+
+    if len(df.multitask.unique()) < 2:
+        pytest.skip('Fixture does not currently contain both multitask and single-task regression tar models.')
+
+    assert set(df.multitask.unique()) == {0, 1}, "Expected both multitask and single-task models"
+
+
+def test_filesystem_perf_results_include_invalid_prediction_columns(sample_result_dir):
+    df = compare_models.get_filesystem_perf_results(
+        str(sample_result_dir),
+        pred_type='regression'
+    )
+
+    expected_cols = {
+        'best_train_invalid_prediction_count',
+        'best_train_invalid_prediction_fraction',
+        'best_train_invalid_prediction_threshold',
+        'best_valid_invalid_prediction_count',
+        'best_valid_invalid_prediction_fraction',
+        'best_valid_invalid_prediction_threshold',
+        'best_test_invalid_prediction_count',
+        'best_test_invalid_prediction_fraction',
+        'best_test_invalid_prediction_threshold',
+    }
+    assert expected_cols.issubset(df.columns)
+
+
+def test_get_multitask_perf_from_files_new_includes_invalid_prediction_columns(sample_result_dir):
+    fs_df = compare_models.get_filesystem_perf_results(
+        str(sample_result_dir),
+        pred_type='regression'
+    )
+    with_invalid = fs_df[~fs_df['best_valid_invalid_prediction_count'].isna()]
+    if with_invalid.empty:
+        pytest.skip('No regression fixture models with invalid prediction metrics available.')
+    dataset_key = with_invalid['dataset_key'].dropna().iloc[0]
+
+    df = compare_models.get_multitask_perf_from_files_new(
+        str(sample_result_dir),
+        pred_type='regression',
+        dataset_key=dataset_key,
+    )
+
+    assert 'best_train_invalid_prediction_count' in df.columns
+    assert 'best_valid_invalid_prediction_fraction' in df.columns
+    assert 'best_test_invalid_prediction_threshold' in df.columns
+
+
+def test_get_multitask_perf_from_files_new_returns_empty_df_for_non_matching_dataset_key(sample_result_dir):
+    df = compare_models.get_multitask_perf_from_files_new(
+        str(sample_result_dir),
+        pred_type='regression',
+        dataset_key='__definitely_not_a_real_dataset_key__.csv',
+    )
+    assert isinstance(df, pd.DataFrame)
+    assert df.empty
+
+
+def test_get_multitask_perf_from_files_new_handles_missing_seed_and_time_built(tmp_path):
+    model_dir = tmp_path / 'model_missing_fields'
+    model_dir.mkdir()
+
+    metadata = {
+        'model_uuid': 'm-1',
+        'splitting_parameters': {
+            'splitter': 'random',
+            'split_strategy': 'train_valid_test',
+            'split_valid_frac': 0.1,
+            'split_test_frac': 0.1,
+            'split_uuid': 'split-1',
+        },
+        'model_parameters': {
+            'ampl_version': '0.0.0-test',
+            'model_type': 'RF',
+            'prediction_type': 'regression',
+            'featurizer': 'ecfp',
+            'descriptor_type': 'none',
+            'num_model_tasks': 1,
+            'production': False,
+            'model_choice_score_type': 'r2',
+        },
+        'training_dataset': {
+            'dataset_key': 'toy.csv',
+            'response_cols': ['y'],
+            'feature_transform_type': 'none',
+            'response_transform_type': 'none',
+            'weight_transform_type': 'none',
+            'smiles_col': 'smiles',
+        },
+        'training_metrics': [
+            {
+                'label': 'best',
+                'subset': 'train',
+                'prediction_results': {
+                    'r2_score': 0.1,
+                    'rms_score': 1.0,
+                    'mae_score': 1.0,
+                    'num_compounds': 4,
+                    'model_choice_score': 0.1,
+                },
+            },
+            {
+                'label': 'best',
+                'subset': 'valid',
+                'prediction_results': {
+                    'r2_score': 0.1,
+                    'rms_score': 1.0,
+                    'mae_score': 1.0,
+                    'num_compounds': 4,
+                    'model_choice_score': 0.1,
+                },
+            },
+            {
+                'label': 'best',
+                'subset': 'test',
+                'prediction_results': {
+                    'r2_score': 0.1,
+                    'rms_score': 1.0,
+                    'mae_score': 1.0,
+                    'num_compounds': 4,
+                    'model_choice_score': 0.1,
+                },
+            },
+        ],
+    }
+
+    metadata_path = model_dir / 'model_metadata.json'
+    with open(metadata_path, 'w') as f:
+        json.dump(metadata, f)
+
+    tar_path = tmp_path / 'model_missing_fields.tar.gz'
+    with tarfile.open(tar_path, mode='w:gz') as tar:
+        tar.add(metadata_path, arcname='model_metadata.json')
+
+    df = compare_models.get_multitask_perf_from_files_new(str(tmp_path), pred_type='regression', tar=True)
+    assert len(df) == 1
+    assert 'seed' in df.columns
+    assert 'time_built' in df.columns
+    assert pd.isna(df['seed'].iloc[0])
+    assert pd.isna(df['time_built'].iloc[0])
 
 # --------------------------
 # main
diff --git a/atomsci/ddm/test/unit/test_featurization.py b/atomsci/ddm/test/unit/test_featurization.py
index 96a61fc0..3ac601c7 100644
--- a/atomsci/ddm/test/unit/test_featurization.py
+++ b/atomsci/ddm/test/unit/test_featurization.py
@@ -1,9 +1,12 @@
 from argparse import Namespace
 import pytest
+import numpy as np
 import atomsci.ddm.pipeline.featurization as feat
 import deepchem as dc
 from atomsci.ddm.pipeline import model_datasets as md
-
+import pandas as pd
+import unittest.mock as mock
+import atomsci.ddm.pipeline.parameter_parser as param_parser
 try:
     from mol_vae_features import MoleculeVAEFeaturizer
     mol_vae_supported = True
@@ -203,5 +206,153 @@ def test_copy_featurizer_params():
     assert hasattr(result, "featurizer")
     assert hasattr(result, "mordred_cpus")
 
+#***********************************************************************************
+@pytest.mark.moe_required
+def test_compute_moe():
+    """Test the compute_all_moe_descriptors function to ensure it correctly generates moe features."""
+    smiles_df = pd.DataFrame({
+        'smiles': ['C(#Cc1c2c(nc3ccccc13)CCCCC2)CCN1CCCCC1', 
+                   'C(#Cc1cccc(CN2CCOCC2)c1)CCN1CCCCC1', 
+                   'C(=C/c1ccccc1)\CN1CCN(C(c2ccccc2)c2ccccc2)CC1'],
+        'compound_id': ['CHEMBL66660', 
+                        'CHEMBL237087', 
+                        'CHEMBL43064'],
+    })
+
+    params = Namespace(
+        smiles_col='smiles',
+        id_col='compound_id',
+        moe_threads=1,
+    )
+
+    moe_df = feat.compute_all_moe_descriptors(
+        smiles_df=smiles_df,
+        params=params)
+
+    assert moe_df is not None
+
+def test_compute_all_mordred_descrs_returns_none_on_valueerror(caplog, monkeypatch):
+    # Fake "res_df" returned by calc.pandas(...)
+    fake_df = mock.MagicMock()
+    fake_df.fill_missing.side_effect = ValueError("boom")
+
+    # Fake calculator
+    fake_calc = mock.MagicMock()
+    fake_calc.pandas.return_value = fake_df
+
+    # Patch the calculator factory used inside the function
+    monkeypatch.setattr(feat, "get_mordred_calculator", lambda ignore_3D: fake_calc)
+
+    with caplog.at_level("ERROR"):
+        out = feat.compute_all_mordred_descrs(mols=[object()])
+
+    assert out is None
+    assert "Mordred descriptor calculation failed in MordredDataFrame.fill_missing" in caplog.text
+
+def test_not_implemented():
+    params = param_parser.wrapper({"dataset_key": "fake.csv"})
+    f = feat.Featurization(params)
+    with pytest.raises(NotImplementedError):
+        f.get_feature_count()
+
+    fp = feat.PersistentFeaturization(params)
+    with pytest.raises(NotImplementedError):
+        fp.featurize_data(
+            dset_df=None,
+            params=None,
+            contains_responses=False
+        )
+
+
+def test_scale_by_heavyatomcount_uses_specified_heavy_atom_col(monkeypatch):
+    params = param_parser.wrapper({
+        "dataset_key": "fake.csv", 
+        "featurizer": "compouted_descriptors",
+        "descriptor_type": "rdkit_raw",})
+    obj = feat.ComputedDescriptorFeaturization(params)
+
+    # Force a small, known descriptor set for the test
+    monkeypatch.setattr(
+        obj.__class__,
+        "desc_type_cols",
+        {"demo": ["MolWt_per_heavyatom", "B_log_scaled", "C"]},
+        raising=False,
+    )
+
+    df = pd.DataFrame(
+        {
+            "id": [1, 2],
+            "HA": [10, 5],               # specified heavy atom column
+            "MolWt": [100.0, 50.0],      # unscaled input for _per_heavyatom
+            "B": [1.0, np.e],            # unscaled input for _log_scaled
+            "C": [7.0, 9.0],             # pass-through
+        }
+    )
+
+    out = obj.scale_by_heavyatomcount_and_log_scale(df, descr_type="demo", heavy_atom_col="HA")
+
+    # Columns exist (do not assert full column order, nondesc_cols order is set-derived)
+    assert {"id", "HA", "MolWt_per_heavyatom", "B_log_scaled", "C"}.issubset(out.columns)
+
+    # Values are correct
+    assert np.allclose(out["MolWt_per_heavyatom"].to_numpy(), np.array([10.0, 10.0]))
+    assert np.allclose(out["B_log_scaled"].to_numpy(), np.array([0.0, 1.0]))
+    assert np.allclose(out["C"].to_numpy(), df["C"].to_numpy())
+    assert np.allclose(out["HA"].to_numpy(), df["HA"].to_numpy())
+    assert np.allclose(out["id"].to_numpy(), df["id"].to_numpy())
+
+
+def test_scale_by_heavyatomcount_raises_runtimeerror_when_cannot_infer_heavy_atom_col(monkeypatch):
+    params = param_parser.wrapper({
+        "dataset_key": "fake.csv", 
+        "featurizer": "compouted_descriptors",
+        "descriptor_type": "rdkit_raw",})
+    obj = feat.ComputedDescriptorFeaturization(params)
+
+    # No "HeavyAtomCount" or "nHeavyAtom" in descr_cols, and we do not pass heavy_atom_col
+    monkeypatch.setattr(
+        obj.__class__,
+        "desc_type_cols",
+        {"demo": ["MolWt_per_heavyatom"]},
+        raising=False,
+    )
+
+    df = pd.DataFrame({"MolWt": [100.0]})
+
+    with pytest.raises(RuntimeError, match=r"Could not infer heavy atom column"):
+        obj.scale_by_heavyatomcount_and_log_scale(df, descr_type="demo")
+
+def test_scale_by_heavyatomcount_and_log_scale_raises_on_negative_log_feature(monkeypatch):
+    params = param_parser.wrapper({
+        "dataset_key": "fake.csv", 
+        "featurizer": "compouted_descriptors",
+        "descriptor_type": "rdkit_raw",})
+    obj = feat.ComputedDescriptorFeaturization(params)
+
+    # No "HeavyAtomCount" or "nHeavyAtom" in descr_cols, and we do not pass heavy_atom_col
+    monkeypatch.setattr(
+        obj.__class__,
+        "desc_type_cols",
+        {"test_type": ["SomeFeature_log_scaled", "HeavyAtomCount"]},
+        raising=False,
+    )
+
+    # Build a DataFrame where:
+    #   - HeavyAtomCount is present so the function can infer heavy atom counts
+    #   - SomeFeature has a negative value, and desc_type_cols includes SomeFeature_log_scaled
+    df = pd.DataFrame({
+        "HeavyAtomCount": [10, 12],
+        "SomeFeature": [-1.0, 2.0]  # negative value triggers ValueError in log scaling
+    })
+
+    with pytest.raises(ValueError) as excinfo:
+        obj.scale_by_heavyatomcount_and_log_scale(
+            desc_df=df,
+            descr_type="test_type"  # uses desc_type_cols defined above
+        )
+
+    # Optionally check the error message
+    assert "Cannot log scale SomeFeature" in str(excinfo.value)
+
 if __name__ == '__main__':
-    test_get_mordred_calculator()
\ No newline at end of file
+    test_compute_moe()
\ No newline at end of file
diff --git a/atomsci/ddm/test/unit/test_generate_transformers.py b/atomsci/ddm/test/unit/test_generate_transformers.py
new file mode 100644
index 00000000..de3c4873
--- /dev/null
+++ b/atomsci/ddm/test/unit/test_generate_transformers.py
@@ -0,0 +1,71 @@
+import atomsci.ddm.utils.generate_transformers as gt
+import tempfile
+import pytest
+
+def test_filter_outlier_features():
+    dataset_key = '../test_datasets/Molport_test.csv'
+    id_col = 'compound_id'
+    smiles_col = 'base_rdkit_smiles'
+    response_cols = 'dummy_response'
+
+    a, b, c = gt.filter_outlier_features(
+        dataset_key, id_col, smiles_col, response_cols, 
+        featurizer='computed_descriptors', descriptor_type='rdkit_raw', 
+        threshold=1e10)
+
+    expected_a = set([
+                  'MolPort-008-351-295', 'MolPort-008-351-371', 'MolPort-008-351-357',
+                  'MolPort-008-351-359', 'MolPort-008-351-292', 'MolPort-008-351-372',
+                  'MolPort-008-351-322', 'MolPort-008-351-275', 'MolPort-008-351-338',
+                  'MolPort-008-351-358', 'MolPort-008-351-382', 'MolPort-008-351-297',
+                  'MolPort-008-351-375', 'MolPort-008-351-354', 'MolPort-008-351-277',
+                  'MolPort-008-351-353', 'MolPort-008-351-318', 'MolPort-008-351-280',
+                  'MolPort-008-351-339', 'MolPort-008-351-343', 'MolPort-008-351-313',
+                  'MolPort-008-351-296', 'MolPort-008-351-336', 'MolPort-008-351-317',
+                  'MolPort-008-351-283', 'MolPort-008-351-373', 'MolPort-008-351-363',
+                  'MolPort-008-351-294', 'MolPort-008-351-290', 'MolPort-008-351-321',
+                  'MolPort-008-351-344', 'MolPort-008-351-305', 'MolPort-008-351-328',
+                  'MolPort-008-351-379', 'MolPort-008-351-352', 'MolPort-008-351-319'])
+    expected_b = ['Ipc']*len(expected_a)
+
+    assert set(a) == expected_a
+    assert list(b) == expected_b
+    assert all([val > 1e10 for val in c])
+
+def test_filter_outlier_MW():
+    dataset_key = '../test_datasets/Molport_test.csv'
+    smiles_col = 'base_rdkit_smiles'
+
+    outliers = gt.filter_outlier_MW(dataset_key, smiles_col, threshold=450, workers=1)
+
+    expected_outliers = ['O=C(CC[C@@H]1NC(=O)N(Cc2ccc3c(c2)OCO3)C1=O)NCCC(c1ccccc1)c1ccccc1', 
+                         'Cc1[nH]c(=O)[nH]c(=O)c1S(=O)(=O)N1CCCC(C(=O)Nc2ccc(C(F)(F)F)cc2)C1', 
+                         'O=C1CC2(C(=O)Nc3ccc(Br)cc32)c2sc(C(=O)O)c(-c3ccccc3)c2N1', 
+                         'O=C(CC[C@@H]1NC(=O)N(Cc2ccc3c(c2)OCO3)C1=O)NC1CCN(Cc2ccccc2)CC1', 
+                         'COC(=O)C[C@H](NC(=O)N1CCc2nc[nH]c2C1c1cccc(Cl)c1Cl)C(=O)OC', 
+                         'Cc1[nH]c(=O)[nH]c(=O)c1S(=O)(=O)N1CCCC(C(=O)NCCc2ccc(Cl)cc2Cl)C1', 
+                         'CN1CCN(C2(CNC(=O)C[C@@H]3NC(=O)N(Cc4ccc5c(c4)OCO5)C3=O)CCCCC2)CC1', 
+                         'O=C(C[C@@H]1NC(=O)N(Cc2ccc3c(c2)OCO3)C1=O)NCc1ccc(Cl)cc1Cl']
+
+    assert outliers == expected_outliers
+
+def test_prepare_csv_and_descriptor_with_dummy_response():
+    dataset_key = '../test_datasets/Molport_test.csv'
+    temp_root = tempfile.mkdtemp()
+
+    # this should raise a RuntimeError
+    # because the split_uuid is used for two split files
+    # this should almost never happen in real life, 
+    # but we want to make sure the error is raised if it does
+    with pytest.raises(RuntimeError):
+        gt.prepare_csv_and_descriptor_with_dummy_response(
+            csv_path=dataset_key,
+            descriptor_type='rdkit_raw',
+            temp_root = temp_root,
+            split_uuid='e28e63fb-4d30-4cef-b303-7f0622f64688'
+        )
+
+if __name__ == "__main__":
+    test_filter_outlier_features()
+    test_filter_outlier_MW()
+    test_prepare_csv_and_descriptor_with_dummy_response()
\ No newline at end of file
diff --git a/atomsci/ddm/test/unit/test_optuna_search.py b/atomsci/ddm/test/unit/test_optuna_search.py
new file mode 100644
index 00000000..cc32ac5a
--- /dev/null
+++ b/atomsci/ddm/test/unit/test_optuna_search.py
@@ -0,0 +1,221 @@
+"""Unit tests for build_optuna_suggest() and Optuna Study checkpoint round-trip."""
+
+import math
+import os
+import pickle
+import tempfile
+from types import SimpleNamespace
+
+import pytest
+import numpy as np
+import pandas as pd
+
+optuna = pytest.importorskip("optuna")
+
+from atomsci.ddm.utils.hyperparam_search_wrapper import build_optuna_suggest
+import atomsci.ddm.utils.hyperparam_search_wrapper as hsw
+
+
+# ---------------------------------------------------------------------------
+# build_optuna_suggest — per-method tests
+# ---------------------------------------------------------------------------
+
+def test_build_optuna_suggest_choice():
+    trial = optuna.trial.FixedTrial({"lr": 0.001})
+    result = build_optuna_suggest(trial, "lr", "choice", [0.0001, 0.001, 0.01])
+    assert result == 0.001
+
+
+def test_build_optuna_suggest_uniform():
+    trial = optuna.trial.FixedTrial({"lr": 0.0005})
+    result = build_optuna_suggest(trial, "lr", "uniform", [0.00001, 0.001])
+    assert result == 0.0005
+
+
+def test_build_optuna_suggest_loguniform():
+    # param_list values are natural-log scale (legacy hyperopt convention);
+    # build_optuna_suggest converts them via math.exp() before calling Optuna.
+    log_low = math.log(0.001)
+    log_high = math.log(1.0)
+    expected = 0.01
+    trial = optuna.trial.FixedTrial({"wdp": expected})
+    result = build_optuna_suggest(trial, "wdp", "loguniform", [log_low, log_high])
+    assert result == expected
+
+
+def test_build_optuna_suggest_uniformint():
+    trial = optuna.trial.FixedTrial({"rfe": 200})
+    result = build_optuna_suggest(trial, "rfe", "uniformint", [64, 512])
+    assert result == 200
+    assert isinstance(result, int)
+
+
+def test_build_optuna_suggest_invalid():
+    trial = optuna.trial.FixedTrial({})
+    with pytest.raises(ValueError):
+        build_optuna_suggest(trial, "x", "badmethod", [0.0, 1.0])
+
+
+# ---------------------------------------------------------------------------
+# Optuna Study checkpoint round-trip
+# ---------------------------------------------------------------------------
+
+def test_checkpoint_roundtrip():
+    study = optuna.create_study(direction="minimize")
+
+    def dummy_objective(trial):
+        x = trial.suggest_float("x", -1.0, 1.0)
+        return x ** 2
+
+    study.optimize(dummy_objective, n_trials=3)
+
+    with tempfile.NamedTemporaryFile(suffix=".pkl", delete=False) as f:
+        ckpt_path = f.name
+    try:
+        with open(ckpt_path, "wb") as f:
+            pickle.dump(study, f)
+
+        with open(ckpt_path, "rb") as f:
+            loaded = pickle.load(f)
+
+        assert len(loaded.trials) == len(study.trials)
+        assert loaded.best_value == study.best_value
+        assert loaded.direction == study.direction
+    finally:
+        os.remove(ckpt_path)
+
+
+class _FakeTrial:
+    def __init__(self):
+        self.user_attrs = {}
+
+    def set_user_attr(self, key, value):
+        self.user_attrs[key] = value
+
+    def suggest_categorical(self, name, choices):
+        return choices[0]
+
+    def suggest_float(self, name, low, high, log=False):
+        return low
+
+    def suggest_int(self, name, low, high):
+        return int(low)
+
+
+class _FakeStudy:
+    def __init__(self):
+        self.trials = []
+
+    def optimize(self, objective, n_trials):
+        for _ in range(n_trials):
+            trial = _FakeTrial()
+            trial.value = objective(trial)
+            self.trials.append(trial)
+
+
+class _FakePerfData:
+    def __init__(self, pred_type):
+        self.pred_type = pred_type
+
+    def get_prediction_results(self):
+        if self.pred_type == 'regression':
+            return {'r2_score': np.nan, 'rms_score': np.inf}
+        return {'roc_auc_score': np.nan, 'accuracy_score': np.inf}
+
+
+class _FakeModelWrapper:
+    def __init__(self, pred_type):
+        self.pred_type = pred_type
+
+    def get_perf_data(self, subset=None, epoch_label=None):
+        return _FakePerfData(self.pred_type)
+
+
+class _FakeModelPipeline:
+    def __init__(self, params):
+        self.params = params
+        self.model_wrapper = _FakeModelWrapper(params.prediction_type)
+
+    def train_model(self):
+        return None
+
+
+def _build_optuna_params(tmpdir, prediction_type):
+    return SimpleNamespace(
+        result_dir=tmpdir,
+        model_type='RF|1',
+        prediction_type=prediction_type,
+        featurizer='ecfp',
+        descriptor_type='moe',
+        hp_checkpoint_load=None,
+        hp_checkpoint_save=None,
+        rfe='choice|10,20',
+        rfd='choice|3,4',
+        rff='choice|5,6',
+        rf_estimators=10,
+        rf_max_depth=3,
+        rf_max_features=5,
+        lr=None,
+        ls=None,
+        ls_ratio=None,
+        dp=None,
+        wdp=None,
+        wdt=None,
+        xgbg=None,
+        xgba=None,
+        xgbb=None,
+        xgbl=None,
+        xgbd=None,
+        xgbc=None,
+        xgbs=None,
+        xgbn=None,
+        xgbw=None,
+        layer_sizes='100,50',
+        dropouts='0.1,0.1',
+        learning_rate=0.001,
+        weight_decay_penalty_type='l2',
+        weight_decay_penalty=1e-05,
+        hyperparam=True,
+    )
+
+
+def test_optuna_objective_sanitizes_non_finite_regression_metrics(monkeypatch):
+    with tempfile.TemporaryDirectory() as td:
+        params = _build_optuna_params(td, 'regression')
+        study = _FakeStudy()
+
+        monkeypatch.setattr(hsw.mp, 'ModelPipeline', _FakeModelPipeline)
+        monkeypatch.setattr(hsw.parse, 'wrapper', lambda d: SimpleNamespace(**d, model_tarball_path='/tmp/fake_reg_model.tar.gz'))
+        monkeypatch.setattr(hsw.optuna, 'create_study', lambda direction='minimize': study)
+
+        search = hsw.OptunaSearch(params)
+        search.run_search()
+
+        perf_files = [f for f in os.listdir(td) if f.startswith('performance_regression_') and f.endswith('.csv')]
+        assert len(perf_files) == 1
+        perf_df = pd.read_csv(os.path.join(td, perf_files[0]))
+        assert perf_df['valid_r2'].iloc[0] == 0
+        assert perf_df['valid_rms'].iloc[0] == 100
+        assert len(study.trials) > 0
+        assert all(np.isfinite([trial.value for trial in study.trials]))
+
+
+def test_optuna_objective_sanitizes_non_finite_classification_metrics(monkeypatch):
+    with tempfile.TemporaryDirectory() as td:
+        params = _build_optuna_params(td, 'classification')
+        study = _FakeStudy()
+
+        monkeypatch.setattr(hsw.mp, 'ModelPipeline', _FakeModelPipeline)
+        monkeypatch.setattr(hsw.parse, 'wrapper', lambda d: SimpleNamespace(**d, model_tarball_path='/tmp/fake_cls_model.tar.gz'))
+        monkeypatch.setattr(hsw.optuna, 'create_study', lambda direction='minimize': study)
+
+        search = hsw.OptunaSearch(params)
+        search.run_search()
+
+        perf_files = [f for f in os.listdir(td) if f.startswith('performance_classification_') and f.endswith('.csv')]
+        assert len(perf_files) == 1
+        perf_df = pd.read_csv(os.path.join(td, perf_files[0]))
+        assert perf_df['valid_roc_auc'].iloc[0] == 0
+        assert perf_df['valid_acc'].iloc[0] == 0
+        assert len(study.trials) > 0
+        assert all(np.isfinite([trial.value for trial in study.trials]))
diff --git a/atomsci/ddm/test/unit/test_parameter_parser_invalid_pred.py b/atomsci/ddm/test/unit/test_parameter_parser_invalid_pred.py
new file mode 100644
index 00000000..6f2c621a
--- /dev/null
+++ b/atomsci/ddm/test/unit/test_parameter_parser_invalid_pred.py
@@ -0,0 +1,101 @@
+import pytest
+from types import SimpleNamespace
+
+import atomsci.ddm.pipeline.parameter_parser as parse
+from atomsci.ddm.pipeline import model_pipeline
+
+
+def test_max_invalid_pred_frac_default_is_set():
+    params = parse.wrapper({
+        'dataset_key': '/tmp/dataset.csv',
+        'bucket': 'public',
+    })
+    assert params.max_invalid_pred_frac == 0.01
+
+
+def test_max_invalid_pred_frac_accepts_valid_values():
+    params = parse.wrapper({
+        'dataset_key': '/tmp/dataset.csv',
+        'bucket': 'public',
+        'max_invalid_pred_frac': 0.25,
+    })
+    assert params.max_invalid_pred_frac == 0.25
+
+
+@pytest.mark.parametrize('bad_value', [-0.01, 1.01])
+def test_max_invalid_pred_frac_rejects_out_of_range_values(bad_value):
+    with pytest.raises(Exception, match='max_invalid_pred_frac must be between 0.0 and 1.0.'):
+        parse.wrapper({
+            'dataset_key': '/tmp/dataset.csv',
+            'bucket': 'public',
+            'max_invalid_pred_frac': bad_value,
+        })
+
+
+def test_create_model_metadata_persists_max_invalid_pred_frac():
+    class _TestParams(SimpleNamespace):
+        def __contains__(self, item):
+            return hasattr(self, item)
+
+    pipe = model_pipeline.ModelPipeline.__new__(model_pipeline.ModelPipeline)
+
+    pipe.params = _TestParams(
+        datastore=False,
+        dataset_key='/tmp/dataset.csv',
+        bucket='public',
+        dataset_hash=None,
+        id_col='compound_id',
+        smiles_col='smiles',
+        response_cols=['y'],
+        robustscaler_with_centering='none',
+        robustscaler_with_scaling='none',
+        robustscaler_quartile_range='none',
+        robustscaler_unit_variance='none',
+        powertransformer_standardize='none',
+        powertransformer_method='none',
+        imputer_strategy='none',
+        feature_transform_type='none',
+        response_transform_type='none',
+        weight_transform_type='none',
+        result_dir='/tmp',
+        production=False,
+        model_bucket='public',
+        system='local',
+        model_type='RF',
+        featurizer='ecfp',
+        prediction_type='regression',
+        model_choice_score_type='r2',
+        max_invalid_pred_frac=0.25,
+        num_model_tasks=1,
+        class_number=2,
+        transformers=[],
+        transformer_key='none',
+        transformer_bucket='public',
+        transformer_oid='oid',
+        uncertainty=False,
+        save_results=False,
+        hyperparam_uuid='hp-uuid',
+        sampling_method=None,
+        sampling_ratio=None,
+        sampling_k_neighbors=None,
+        model_uuid='model-uuid',
+    )
+
+    pipe.data = SimpleNamespace(
+        dataset_oid='dataset-oid',
+        get_split_metadata=lambda: {
+            'splitter': 'random',
+            'split_strategy': 'train_valid_test',
+            'split_valid_frac': 0.1,
+            'split_test_frac': 0.1,
+            'split_uuid': 'split-uuid',
+        },
+        featurization=SimpleNamespace(get_feature_specific_metadata=lambda params: {}),
+    )
+    pipe.model_wrapper = SimpleNamespace(get_model_specific_metadata=lambda: {})
+    pipe.seed = 7
+
+    pipe.create_model_metadata()
+
+    assert 'model_parameters' in pipe.model_metadata
+    assert pipe.model_metadata['model_parameters']['max_invalid_pred_frac'] == 0.25
diff --git a/atomsci/ddm/test/unit/test_patch_model_dataset_key.py b/atomsci/ddm/test/unit/test_patch_model_dataset_key.py
index bee27b17..0797421c 100644
--- a/atomsci/ddm/test/unit/test_patch_model_dataset_key.py
+++ b/atomsci/ddm/test/unit/test_patch_model_dataset_key.py
@@ -98,7 +98,13 @@ def test_check_data_accessibility_folder():
     )
 
     dataset_info = pmdk.check_data_accessibility(model_path=model_path)
-    assert len(dataset_info) == 4
+    expected_tar_count = len([
+        entry for entry in os.scandir(model_path)
+        if entry.is_file() and entry.name.endswith('.tar.gz')
+    ])
+
+    assert expected_tar_count > 0
+    assert len(dataset_info) == expected_tar_count
 
 def test_check_data_accessibility_bad():
     # directory with bad tarball
diff --git a/atomsci/ddm/test/unit/test_perf_data.py b/atomsci/ddm/test/unit/test_perf_data.py
index f5752abc..2a697eec 100644
--- a/atomsci/ddm/test/unit/test_perf_data.py
+++ b/atomsci/ddm/test/unit/test_perf_data.py
@@ -8,6 +8,9 @@
 import shutil
 import pandas as pd
 import json
+from types import SimpleNamespace
+
+import pytest
 
 def copy_to_temp(dskey, res_dir):
     """
@@ -320,6 +323,269 @@ def test_SimpleClassificationPerfData():
     assert roc_auc_mean==1
 
 
+def test_safe_regression_score_filters_when_invalid_fraction_within_threshold():
+    perf_data._configure_invalid_pred_frac_threshold(SimpleNamespace(max_invalid_pred_frac=0.50))
+
+    y_real = np.array([0.0, 1.0, 2.0, 3.0], dtype=float)
+    y_pred = np.array([0.0, 1.0, np.nan, 3.0], dtype=float)
+    score = perf_data._safe_regression_score('r2', y_real, y_pred)
+
+    assert np.isclose(score, 1.0)
+
+
+def test_safe_regression_score_penalizes_when_invalid_fraction_exceeds_threshold():
+    perf_data._configure_invalid_pred_frac_threshold(SimpleNamespace(max_invalid_pred_frac=0.10))
+
+    y_real = np.array([0.0, 1.0, 2.0, 3.0], dtype=float)
+    y_pred = np.array([0.0, 1.0, np.nan, 3.0], dtype=float)
+    score = perf_data._safe_regression_score('r2', y_real, y_pred)
+
+    assert score == -1.0e12
+
+
+@pytest.mark.parametrize(
+    'score_type,expected_penalty',
+    [('mae', 1.0e12), ('rmse', 1.0e12)],
+)
+def test_safe_regression_score_penalty_direction_for_loss_metrics(score_type, expected_penalty):
+    perf_data._configure_invalid_pred_frac_threshold(SimpleNamespace(max_invalid_pred_frac=0.10))
+
+    y_real = np.array([0.0, 1.0, 2.0, 3.0], dtype=float)
+    y_pred = np.array([0.0, np.nan, np.nan, 3.0], dtype=float)
+    score = perf_data._safe_regression_score(score_type, y_real, y_pred)
+
+    assert score == expected_penalty
+
+
+def test_safe_classification_score_filters_when_invalid_fraction_within_threshold():
+    perf_data._configure_invalid_pred_frac_threshold(SimpleNamespace(max_invalid_pred_frac=0.50))
+
+    y_real = np.array([0, 1, 1, 0], dtype=int)
+    y_pred = np.array([0, 1, np.nan, 0], dtype=float)
+    score = perf_data._safe_classification_score('accuracy', y_real, y_pred)
+
+    assert np.isclose(score, 1.0)
+
+
+def test_safe_classification_score_penalizes_when_invalid_fraction_exceeds_threshold():
+    perf_data._configure_invalid_pred_frac_threshold(SimpleNamespace(max_invalid_pred_frac=0.10))
+
+    y_real = np.array([0, 1, 1, 0], dtype=int)
+    y_pred = np.array([0, 1, np.nan, 0], dtype=float)
+    score = perf_data._safe_classification_score('accuracy', y_real, y_pred)
+
+    assert score == 0.0
+
+
+@pytest.mark.parametrize(
+    'score_type,y_real,y_pred,expected_value',
+    [
+        ('roc_auc', np.array([0, 1, 0, 1], dtype=int), np.array([0.1, 0.9, np.nan, 0.8], dtype=float), 1.0),
+        ('avg_precision', np.array([0, 1, 0, 1], dtype=int), np.array([0.1, 0.9, np.nan, 0.8], dtype=float), 1.0),
+    ],
+)
+def test_safe_classification_probability_metrics_filter_small_invalid_fraction(score_type, y_real, y_pred, expected_value):
+    perf_data._configure_invalid_pred_frac_threshold(SimpleNamespace(max_invalid_pred_frac=0.50))
+
+    score = perf_data._safe_classification_score(score_type, y_real, y_pred)
+    assert np.isclose(score, expected_value)
+
+
+def test_safe_classification_cross_entropy_filter_small_invalid_fraction():
+    perf_data._configure_invalid_pred_frac_threshold(SimpleNamespace(max_invalid_pred_frac=0.50))
+
+    y_real = np.array([0, 1, 0, 1], dtype=int)
+    y_pred = np.array([0.2, 0.8, np.nan, 0.7], dtype=float)
+    score = perf_data._safe_classification_score('cross_entropy', y_real, y_pred)
+    expected = perf_data.log_loss(np.array([0, 1, 1]), np.array([0.2, 0.8, 0.7]))
+    assert np.isclose(score, expected)
+
+
+def test_safe_classification_cross_entropy_penalty_when_invalid_fraction_exceeds_threshold():
+    perf_data._configure_invalid_pred_frac_threshold(SimpleNamespace(max_invalid_pred_frac=0.10))
+
+    y_real = np.array([0, 1, 0, 1], dtype=int)
+    y_pred = np.array([0.2, np.nan, np.nan, 0.7], dtype=float)
+    score = perf_data._safe_classification_score('cross_entropy', y_real, y_pred)
+    assert score == 1.0e12
+
+
+def test_safe_regression_score_returns_penalty_for_all_invalid_rows():
+    perf_data._configure_invalid_pred_frac_threshold(SimpleNamespace(max_invalid_pred_frac=0.99))
+
+    y_real = np.array([np.nan, np.nan], dtype=float)
+    y_pred = np.array([np.nan, np.nan], dtype=float)
+    score = perf_data._safe_regression_score('r2', y_real, y_pred)
+    assert score == -1.0e12
+
+
+def test_safe_classification_score_returns_penalty_for_shape_mismatch():
+    perf_data._configure_invalid_pred_frac_threshold(SimpleNamespace(max_invalid_pred_frac=0.50))
+
+    y_real = np.array([0, 1, 0], dtype=int)
+    y_pred = np.array([0.1, np.nan], dtype=float)
+    score = perf_data._safe_classification_score('accuracy', y_real, y_pred)
+    assert score == 0.0
+
+
+def test_coerce_invalid_pred_classes_binary_sets_wrong_class_for_invalid_rows():
+    y_real = np.array([0, 1, 1, 0], dtype=int)
+    y_pred = np.array([0, 1, 0, 0], dtype=int)
+    class_probs = np.array([0.1, np.inf, 0.8, np.nan], dtype=float)
+
+    coerced = perf_data._coerce_invalid_pred_classes(y_real, y_pred, class_probs, 2)
+
+    assert np.array_equal(coerced, np.array([0, 0, 0, 1]))
+
+
+def test_safe_confusion_matrix_uses_coerced_classes_not_zeroed_when_real_is_finite():
+    y_real = np.array([0, 1, 1, 0], dtype=int)
+    y_pred = np.array([0, 0, 0, 1], dtype=int)
+
+    cm = perf_data._safe_confusion_matrix(y_real, y_pred, 2)
+    assert cm == [[1, 1], [2, 0]]
+
+
+def test_max_invalid_pred_frac_clamps_out_of_range_values():
+    perf_data._configure_invalid_pred_frac_threshold(SimpleNamespace(max_invalid_pred_frac=2.5))
+    assert perf_data._get_active_invalid_pred_frac_threshold() == 1.0
+
+    perf_data._configure_invalid_pred_frac_threshold(SimpleNamespace(max_invalid_pred_frac=-0.2))
+    assert perf_data._get_active_invalid_pred_frac_threshold() == 0.0
+
+
+def test_simple_regression_prediction_results_include_invalid_prediction_fields():
+    class _RegDataset:
+        def __init__(self):
+            self.ids = np.array([0, 1, 2, 3])
+            self.y = np.array([[0.0], [1.0], [2.0], [3.0]], dtype=float)
+            self.w = np.ones((4, 1), dtype=float)
+
+    class _RegModelDataset:
+        def __init__(self):
+            dset = _RegDataset()
+            self.train_valid_dsets = [(dset, dset)]
+            self.dataset = dset
+            self.test_dset = dset
+            self._responses = dset.y
+
+        def get_untransformed_responses(self, ids):
+            return self._responses[np.array(ids, dtype=int)]
+
+    model_dataset = _RegModelDataset()
+    perf = perf_data.SimpleRegressionPerfData(model_dataset, subset='train')
+    real_vals = perf.get_real_values()
+    ids = model_dataset.train_valid_dsets[0][0].ids
+    pred_vals = real_vals.copy()
+    pred_vals[0, 0] = np.nan
+
+    perf.accumulate_preds(pred_vals, ids)
+    pred_results = perf.get_prediction_results()
+
+    for key in [
+        'invalid_prediction_count',
+        'invalid_prediction_fraction',
+        'invalid_prediction_threshold',
+        'task_invalid_prediction_counts',
+        'task_invalid_prediction_fractions',
+    ]:
+        assert key in pred_results
+
+    assert pred_results['invalid_prediction_count'] == 1
+    assert np.isclose(pred_results['invalid_prediction_fraction'], 1.0 / len(real_vals))
+    assert len(pred_results['task_invalid_prediction_counts']) == perf.num_tasks
+
+
+def test_simple_classification_prediction_results_include_invalid_prediction_fields():
+    class _ClsDataset:
+        def __init__(self):
+            self.ids = np.array([0, 1, 2, 3])
+            self.y = np.array([[0], [1], [0], [1]], dtype=int)
+            self.w = np.ones((4, 1), dtype=float)
+
+    class _ClsModelDataset:
+        def __init__(self):
+            dset = _ClsDataset()
+            self.train_valid_dsets = [(dset, dset)]
+            self.dataset = dset
+            self.test_dset = dset
+            self._responses = dset.y
+
+        def get_untransformed_responses(self, ids):
+            return self._responses[np.array(ids, dtype=int)]
+
+    model_dataset = _ClsModelDataset()
+    perf = perf_data.SimpleClassificationPerfData(model_dataset, subset='train')
+    ids = model_dataset.train_valid_dsets[0][0].ids
+
+    pred_vals = np.array([
+        [[1.0, 0.0]],
+        [[0.0, 1.0]],
+        [[1.0, 0.0]],
+        [[0.0, 1.0]],
+    ], dtype=float)
+    pred_vals[0, 0, 1] = np.nan
+
+    perf.accumulate_preds(pred_vals, ids=ids)
+    pred_results = perf.get_prediction_results()
+
+    for key in [
+        'invalid_prediction_count',
+        'invalid_prediction_fraction',
+        'invalid_prediction_threshold',
+        'task_invalid_prediction_counts',
+        'task_invalid_prediction_fractions',
+    ]:
+        assert key in pred_results
+
+    assert pred_results['invalid_prediction_count'] == 1
+    assert np.isclose(pred_results['invalid_prediction_fraction'], 0.25)
+
+
+def test_simple_hybrid_prediction_results_include_invalid_prediction_fields():
+    class _HybridDataset:
+        def __init__(self):
+            self.ids = np.array([0, 1, 2, 3])
+            self.y = np.array([
+                [7.0, np.nan],
+                [6.5, np.nan],
+                [0.8, 50.0],
+                [0.2, 50.0],
+            ], dtype=float)
+            self.w = np.ones((4, 2), dtype=float)
+
+    class _HybridModelDataset:
+        def __init__(self):
+            dset = _HybridDataset()
+            self.train_valid_dsets = [(dset, dset)]
+            self.dataset = dset
+            self.test_dset = dset
+            self._responses = dset.y
+
+        def get_untransformed_responses(self, ids):
+            return self._responses[np.array(ids, dtype=int)]
+
+    model_dataset = _HybridModelDataset()
+    perf = perf_data.SimpleHybridPerfData(model_dataset, subset='train', is_ki=False)
+    pred_vals = model_dataset._responses.copy()
+    pred_vals[0, 0] = np.nan
+
+    perf.accumulate_preds(pred_vals, ids=model_dataset.train_valid_dsets[0][0].ids)
+    pred_results = perf.get_prediction_results()
+
+    for key in [
+        'invalid_prediction_count',
+        'invalid_prediction_fraction',
+        'invalid_prediction_threshold',
+        'task_invalid_prediction_counts',
+        'task_invalid_prediction_fractions',
+    ]:
+        assert key in pred_results
+
+    assert pred_results['invalid_prediction_count'] == 1
+    assert np.isclose(pred_results['invalid_prediction_fraction'], 0.25)
+
+
 if __name__ == "__main__":
     test_KFoldRegressionPerfDataMulti()
     test_KFoldRegressionPerfData()
diff --git a/atomsci/ddm/test/unit/test_perf_plots.py b/atomsci/ddm/test/unit/test_perf_plots.py
index 2ccc6ac4..14ab1b87 100644
--- a/atomsci/ddm/test/unit/test_perf_plots.py
+++ b/atomsci/ddm/test/unit/test_perf_plots.py
@@ -2,12 +2,16 @@
 from contextlib import contextmanager
 import matplotlib.pyplot as plt
 from unittest.mock import MagicMock
+import tarfile
+import json
 
 import pytest
 import pandas as pd
 
 from matplotcheck.base import PlotTester
-from atomsci.ddm.pipeline.perf_plots import plot_pred_vs_actual_from_df
+import atomsci.ddm.pipeline.perf_plots as perf_plots
+import io
+import numpy as np
 
 # --- Fixtures ---
 @pytest.fixture
@@ -92,7 +96,7 @@ def test_plot_pred_vs_actual_from_df_basic(mock_data, mock_plot):
     df = mock_data
 
     with mock_plot() as mock_scatter:
-        ax = plot_pred_vs_actual_from_df(df, actual_col='avg_pIC50_actual', pred_col='avg_pIC50_pred')
+        ax = perf_plots.plot_pred_vs_actual_from_df(df, actual_col='avg_pIC50_actual', pred_col='avg_pIC50_pred')
         
         # Verify the plot was created
         assert ax is not None
@@ -124,7 +128,7 @@ def test_plot_pred_vs_actual_from_df_with_std(mock_data, mock_plot):
     """
     df = mock_data
     with mock_plot() as mock_scatter:
-        ax = plot_pred_vs_actual_from_df(df, actual_col='avg_pIC50_actual', pred_col='avg_pIC50_pred', std_col='avg_pIC50_std')
+        ax = perf_plots.plot_pred_vs_actual_from_df(df, actual_col='avg_pIC50_actual', pred_col='avg_pIC50_pred', std_col='avg_pIC50_std')
         
         pt = PlotTester(ax)
         assert ax is not None
@@ -149,7 +153,7 @@ def test_plot_pred_vs_actual_from_df_with_label(mock_data, mock_plot):
     """
     df = mock_data
     with mock_plot() as mock_scatter:
-        ax = plot_pred_vs_actual_from_df(df, actual_col='avg_pIC50_actual', pred_col='avg_pIC50_pred', label='Test Label')
+        ax = perf_plots.plot_pred_vs_actual_from_df(df, actual_col='avg_pIC50_actual', pred_col='avg_pIC50_pred', label='Test Label')
         
         pt = PlotTester(ax)
         assert ax is not None
@@ -176,7 +180,7 @@ def test_plot_pred_vs_actual_from_df_with_threshold(mock_data, mock_plot):
 
     df = mock_data
     with mock_plot() as mock_scatter:
-        ax = plot_pred_vs_actual_from_df(df, actual_col='avg_pIC50_actual', pred_col='avg_pIC50_pred', threshold=7.0)
+        ax = perf_plots.plot_pred_vs_actual_from_df(df, actual_col='avg_pIC50_actual', pred_col='avg_pIC50_pred', threshold=7.0)
         
         pt = PlotTester(ax)
         assert ax is not None
@@ -203,7 +207,7 @@ def test_plot_pred_vs_actual_from_df_with_all_options(mock_data, mock_plot):
     """
     df = mock_data
     with mock_plot() as mock_scatter:
-        ax = plot_pred_vs_actual_from_df(df, actual_col='avg_pIC50_actual', pred_col='avg_pIC50_pred', std_col='avg_pIC50_std', label='Test Label', threshold=7.0)
+        ax = perf_plots.plot_pred_vs_actual_from_df(df, actual_col='avg_pIC50_actual', pred_col='avg_pIC50_pred', std_col='avg_pIC50_std', label='Test Label', threshold=7.0)
         
         pt = PlotTester(ax)
         assert ax is not None
@@ -211,4 +215,345 @@ def test_plot_pred_vs_actual_from_df_with_all_options(mock_data, mock_plot):
         pt.assert_axis_label_contains("y", "avg_pIC50_pred")
         pt.assert_plot_type("scatter")
         assert ax.get_title() == 'Test Label'
-        assert any(line.get_linestyle() == '--' for line in ax.get_lines())
\ No newline at end of file
+        assert any(line.get_linestyle() == '--' for line in ax.get_lines())
+
+
+def _make_model_tarball(tmp_path, metadata: dict, name="model.tar.gz"):
+    """Create a minimal tar.gz containing model_metadata.json."""
+    model_path = tmp_path / name
+    meta_bytes = json.dumps(metadata).encode("utf-8")
+
+    with tarfile.open(model_path, mode="w:gz") as tar:
+        ti = tarfile.TarInfo(name="model_metadata.json")
+        ti.size = len(meta_bytes)
+        tar.addfile(ti, io.BytesIO(meta_bytes))
+    return str(model_path)
+
+
+def _write_csv(path, df: pd.DataFrame):
+    df.to_csv(path, index=False)
+    return str(path)
+
+
+# -------------------------------
+# Tests: merge_response_cols_from_original
+# -------------------------------
+
+def test_merge_response_cols_happy_path_adds_missing_response_cols():
+    feat_df = pd.DataFrame(
+        {
+            "cmpd_id": ["1", "2", "3"],
+            "feat1": [0.1, 0.2, 0.3],
+        }
+    )
+    orig_df = pd.DataFrame(
+        {
+            "cmpd_id": ["1", "2", "3"],
+            "y": [10.0, 20.0, 30.0],
+        }
+    )
+
+    out = perf_plots.merge_response_cols_from_original(
+        feat_df, orig_df, id_col="cmpd_id", response_cols=["y"]
+    )
+
+    assert "y" in out.columns
+    assert out.loc[out.cmpd_id == "2", "y"].iloc[0] == 20.0
+
+
+def test_merge_response_cols_does_not_overwrite_existing_columns():
+    feat_df = pd.DataFrame(
+        {
+            "cmpd_id": ["1", "2"],
+            "y": [999.0, 999.0],  # pre-existing, should not be overwritten
+        }
+    )
+    orig_df = pd.DataFrame({"cmpd_id": ["1", "2"], "y": [1.0, 2.0]})
+
+    out = perf_plots.merge_response_cols_from_original(
+        feat_df, orig_df, id_col="cmpd_id", response_cols=["y"]
+    )
+
+    assert out["y"].tolist() == [999.0, 999.0]
+
+
+def test_merge_response_cols_raises_when_id_col_missing_in_feat():
+    feat_df = pd.DataFrame({"not_id": [1, 2], "feat": [0.1, 0.2]})
+    orig_df = pd.DataFrame({"cmpd_id": [1, 2], "y": [1.0, 2.0]})
+
+    with pytest.raises(KeyError):
+        perf_plots.merge_response_cols_from_original(
+            feat_df, orig_df, id_col="cmpd_id", response_cols=["y"]
+        )
+
+
+def test_merge_response_cols_missing_frac_exceeds_threshold_raises():
+    # orig has 4 unique IDs, feat only contains 1 => 75% missing
+    feat_df = pd.DataFrame({"cmpd_id": ["1"], "feat": [0.1]})
+    orig_df = pd.DataFrame({"cmpd_id": ["1", "2", "3", "4"], "y": [1, 2, 3, 4]})
+
+    with pytest.raises(ValueError, match="exceeds allowed"):
+        perf_plots.merge_response_cols_from_original(
+            feat_df,
+            orig_df,
+            id_col="cmpd_id",
+            response_cols=["y"],
+            max_missing_frac=0.10,
+            sample_n=2,
+        )
+
+
+def test_merge_response_cols_extra_ids_warns_by_default():
+    feat_df = pd.DataFrame({"cmpd_id": ["1", "X"], "feat": [0.1, 0.9]})
+    orig_df = pd.DataFrame({"cmpd_id": ["1"], "y": [1.0]})
+
+    # Keep missing_frac low: orig_set - feat_set = {} so missing_frac=0
+    with pytest.warns(UserWarning, match="contains 1 unique compound IDs not found"):
+        perf_plots.merge_response_cols_from_original(
+            feat_df,
+            orig_df,
+            id_col="cmpd_id",
+            response_cols=["y"],
+            max_missing_frac=0.50,
+            error_on_extra_feat_ids=False,
+        )
+
+
+def test_merge_response_cols_extra_ids_raises_if_configured():
+    feat_df = pd.DataFrame({"cmpd_id": ["1", "X"], "feat": [0.1, 0.9]})
+    orig_df = pd.DataFrame({"cmpd_id": ["1"], "y": [1.0]})
+
+    with pytest.raises(ValueError, match="not found in original"):
+        perf_plots.merge_response_cols_from_original(
+            feat_df,
+            orig_df,
+            id_col="cmpd_id",
+            response_cols=["y"],
+            max_missing_frac=0.50,
+            error_on_extra_feat_ids=True,
+        )
+
+
+def test_merge_response_cols_coerces_id_to_str_by_default():
+    feat_df = pd.DataFrame({"cmpd_id": [1, 2], "feat": [0.1, 0.2]})
+    orig_df = pd.DataFrame({"cmpd_id": ["1", "2"], "y": [10.0, 20.0]})
+
+    out = perf_plots.merge_response_cols_from_original(
+        feat_df, orig_df, id_col="cmpd_id", response_cols=["y"], coerce_id_to_str=True
+    )
+    assert out["y"].tolist() == [10.0, 20.0]
+
+
+def test_merge_response_cols_returns_feat_unchanged_if_no_responses_available():
+    feat_df = pd.DataFrame({"cmpd_id": ["1"], "feat": [0.1]})
+    orig_df = pd.DataFrame({"cmpd_id": ["1"], "other": [9]})
+
+    out = perf_plots.merge_response_cols_from_original(
+        feat_df, orig_df, id_col="cmpd_id", response_cols=["y"]
+    )
+    assert list(out.columns) == ["cmpd_id", "feat"]
+
+
+# -------------------------------
+# Tests: plot_pred_vs_actual_from_file
+# -------------------------------
+
+def test_plot_pred_vs_actual_from_file_raises_for_classification(tmp_path):
+    # Minimal config for classification should raise early
+    dataset_csv = tmp_path / "data.csv"
+    _write_csv(dataset_csv, pd.DataFrame({"cmpd_id": ["1"], "smiles": ["C"], "y": [1]}))
+
+    cfg = {
+        "model_parameters": {"prediction_type": "classification", "model_type": "rf", "featurizer": "ecfp"},
+        "training_dataset": {"dataset_key": str(dataset_csv), "response_cols": ["y"], "id_col": "cmpd_id", "smiles_col": "smiles"},
+        "splitting_parameters": {"split_uuid": "uuid", "splitter": "random", "split_strategy": "train_valid_test"},
+        "training_metrics": [],
+    }
+    model_path = _make_model_tarball(tmp_path, cfg)
+
+    with pytest.raises(ValueError, match="only be called for regression models"):
+        perf_plots.plot_pred_vs_actual_from_file(model_path)
+
+
+def test_plot_pred_vs_actual_from_file_builds_figure_and_r2_titles(tmp_path, monkeypatch):
+    # Prepare dataset and split file
+    dataset_csv = tmp_path / "data.csv"
+    df = pd.DataFrame(
+        {
+            "cmpd_id": ["1", "2", "3", "4", "5", "6"],
+            "smiles": ["C", "CC", "CCC", "CCCC", "CCO", "CO"],
+            "y": [0.0, 1.0, 2.0, 3.0, 4.0, 5.0],
+        }
+    )
+    _write_csv(dataset_csv, df)
+
+    split_uuid = "split-uuid-123"
+    split_csv = tmp_path / f"anything_{split_uuid}_split.csv"
+    split_df = pd.DataFrame(
+        {
+            "cmpd_id": ["1", "2", "3", "4", "5", "6"],
+            "subset": ["train", "train", "valid", "valid", "test", "test"],
+        }
+    )
+    _write_csv(split_csv, split_df)
+
+    cfg = {
+        "model_parameters": {
+            "prediction_type": "regression",
+            "model_type": "rf",
+            "featurizer": "ecfp",
+            # keep uncertainty absent so error_bars becomes False even if requested
+        },
+        "training_dataset": {
+            "dataset_key": str(dataset_csv),
+            "response_cols": ["y"],
+            "id_col": "cmpd_id",
+            "smiles_col": "smiles",
+        },
+        "splitting_parameters": {
+            "split_uuid": split_uuid,
+            "splitter": "random",
+            "split_strategy": "train_valid_test",
+        },
+        "training_metrics": [],
+    }
+    model_path = _make_model_tarball(tmp_path, cfg)
+
+    # Patch safe_extract to allow tar extraction without depending on atomsci futils implementation
+    def _safe_extract_passthrough(tar, path):
+        tar.extractall(path=path)
+
+    monkeypatch.setattr(perf_plots.futils, "safe_extract", _safe_extract_passthrough)
+
+    # Patch prediction to return a dataframe with required columns: y_actual, y_pred and subset
+    def _fake_predict_from_model_file(model_path_in, input_df, **kwargs):
+        out = input_df.copy()
+        out["y_actual"] = out["y"].astype(float)
+        out["y_pred"] = out["y_actual"]  # perfect predictions => r2 = 1.0 on each subset
+        return out
+
+    monkeypatch.setattr(perf_plots.pfm, "predict_from_model_file", _fake_predict_from_model_file)
+
+    fig = perf_plots.plot_pred_vs_actual_from_file(model_path, plot_size=3, error_bars=True)
+    assert fig is not None
+
+    # Expect 1 response * 3 subsets = 3 axes
+    assert len(fig.axes) == 3
+
+    titles = [ax.get_title() for ax in fig.axes]
+    # First plot has "y train, R^2 = 1.000" style title, others "valid, R^2 = 1.000", "test, ..."
+    assert any("R^2" in t for t in titles)
+    assert any("1.000" in t for t in titles)
+
+    plt.close(fig)
+
+
+def test_plot_pred_vs_actual_from_file_uses_external_training_data(tmp_path, monkeypatch):
+    # Original dataset_key in metadata points to one CSV, but we override via external_training_data.
+    dataset_csv_original = tmp_path / "data_original.csv"
+    dataset_csv_external = tmp_path / "data_external.csv"
+
+    df_orig = pd.DataFrame({"cmpd_id": ["1"], "smiles": ["C"], "y": [1.0]})
+    _write_csv(dataset_csv_original, df_orig)
+
+    df_ext = pd.DataFrame({
+        "cmpd_id": ["1", "2", "3"],
+        "smiles": ["C", "CC", "CCC"],
+        "y": [2.0, 3.0, 4.0],
+    })
+    _write_csv(dataset_csv_external, df_ext)
+
+    split_uuid = "uuid2"
+    split_csv = tmp_path / f"split_{split_uuid}.csv"
+    _write_csv(split_csv, pd.DataFrame({
+        "cmpd_id": ["1", "2", "3"],
+        "subset": ["train", "valid", "test"],
+    }))
+
+    cfg = {
+        "model_parameters": {"prediction_type": "regression", "model_type": "rf", "featurizer": "ecfp"},
+        "training_dataset": {"dataset_key": str(dataset_csv_original), "response_cols": ["y"], "id_col": "cmpd_id", "smiles_col": "smiles"},
+        "splitting_parameters": {"split_uuid": split_uuid, "splitter": "random", "split_strategy": "train_valid_test"},
+        "training_metrics": [],
+    }
+    model_path = _make_model_tarball(tmp_path, cfg)
+
+    def _safe_extract_passthrough(tar, path):
+        tar.extractall(path=path)
+
+    monkeypatch.setattr(perf_plots.futils, "safe_extract", _safe_extract_passthrough)
+
+    seen = {"used_y": None}
+
+    def _fake_predict_from_model_file(model_path_in, input_df, **kwargs):
+        # Record which dataset was used by checking y
+        seen["used_y"] = float(input_df["y"].iloc[0])
+        out = input_df.copy()
+        out["y_actual"] = out["y"].astype(float)
+        out["y_pred"] = out["y_actual"]
+        return out
+
+    monkeypatch.setattr(perf_plots.pfm, "predict_from_model_file", _fake_predict_from_model_file)
+
+    fig = perf_plots.plot_pred_vs_actual_from_file(
+        model_path,
+        external_training_data=str(dataset_csv_external),
+        plot_size=3,
+    )
+
+    assert seen["used_y"] == 2.0  # proves override worked
+    plt.close(fig)
+
+
+def test_plot_pred_vs_actual_from_file_descriptor_path_rewrite(tmp_path, monkeypatch):
+    # If featurizer is descriptors/computed_descriptors, function rewrites dataset path to scaled_descriptors/..._with_<desc>_descriptors.csv
+    data_dir = tmp_path
+    orig = data_dir / "data.csv"
+    _write_csv(orig, pd.DataFrame({"cmpd_id": ["1"], "smiles": ["C"], "y": [1.0]}))
+
+    # Create the expected rewritten file
+    scaled_dir = data_dir / "scaled_descriptors"
+    scaled_dir.mkdir()
+    rewritten = scaled_dir / "data_with_mordred_descriptors.csv"
+
+    df_rewritten = pd.DataFrame({
+        "cmpd_id": ["1", "2", "3"],
+        "smiles": ["C", "CC", "CCC"],
+        "y": [2.0, 3.0, 4.0],
+    })
+    _write_csv(rewritten, df_rewritten)
+
+    split_uuid = "uuid3"
+    split_csv = data_dir / f"split_{split_uuid}.csv"
+    _write_csv(split_csv, pd.DataFrame({
+        "cmpd_id": ["1", "2", "3"],
+        "subset": ["train", "valid", "test"],
+    }))
+
+    cfg = {
+        "model_parameters": {"prediction_type": "regression", "model_type": "rf", "featurizer": "descriptors"},
+        "descriptor_specific": {"descriptor_type": "mordred"},
+        "training_dataset": {"dataset_key": str(orig), "response_cols": ["y"], "id_col": "cmpd_id", "smiles_col": "smiles"},
+        "splitting_parameters": {"split_uuid": split_uuid, "splitter": "random", "split_strategy": "train_valid_test"},
+        "training_metrics": [],
+    }
+    model_path = _make_model_tarball(tmp_path, cfg)
+
+    def _safe_extract_passthrough(tar, path):
+        tar.extractall(path=path)
+
+    monkeypatch.setattr(perf_plots.futils, "safe_extract", _safe_extract_passthrough)
+
+    # We must ensure merge_response_cols_from_original is exercised: rewritten df lacks y, orig has y.
+    def _fake_predict_from_model_file(model_path_in, input_df, **kwargs):
+        assert "y" in input_df.columns  # confirms merge happened
+        out = input_df.copy()
+        out["y_actual"] = out["y"].astype(float)
+        out["y_pred"] = out["y_actual"]
+        return out
+
+    monkeypatch.setattr(perf_plots.pfm, "predict_from_model_file", _fake_predict_from_model_file)
+
+    fig = perf_plots.plot_pred_vs_actual_from_file(model_path, plot_size=3)
+    assert fig is not None
+    plt.close(fig)
\ No newline at end of file
diff --git a/atomsci/ddm/test/unit/test_prediction_order.py b/atomsci/ddm/test/unit/test_prediction_order.py
index ae17a4ee..0caa4012 100644
--- a/atomsci/ddm/test/unit/test_prediction_order.py
+++ b/atomsci/ddm/test/unit/test_prediction_order.py
@@ -13,7 +13,7 @@ def test_predict_from_model():
     """test that predict_from_model makes predictions in the same
     order as the input
     """
-    model_path = '../../examples/BSEP/models/bsep_classif_scaffold_split.tar.gz'
+    model_path = '../../examples/BSEP/models/bsep_classif_scaffold_split_graphconv.tar.gz'
     csv_path = '../../examples/BSEP/data/ChEMBL25_BSEP_curated_data.csv'
 
     id_col = 'compound_id'
@@ -44,7 +44,7 @@ def test_predict_full_dataset():
     """test that predict_full_dataset makes predictions in the same
     order as the input
     """
-    model_path = '../../examples/BSEP/models/bsep_classif_scaffold_split.tar.gz'
+    model_path = '../../examples/BSEP/models/bsep_classif_scaffold_split_graphconv.tar.gz'
     csv_path = '../../examples/BSEP/data/ChEMBL25_BSEP_curated_data.csv'
 
     id_col = 'compound_id'
diff --git a/atomsci/ddm/test/unit/test_struct_utils.py b/atomsci/ddm/test/unit/test_struct_utils.py
index e9ac3004..5ce7463e 100644
--- a/atomsci/ddm/test/unit/test_struct_utils.py
+++ b/atomsci/ddm/test/unit/test_struct_utils.py
@@ -20,13 +20,13 @@ def test_get_rdkit_smiles():
 
 def test_rdkit_smiles_from_smiles():
     result = su.rdkit_smiles_from_smiles(test_smiles, useCanonicalTautomers=True)
-    assert result == ['Brc1cc(O[C@@H]2C[C@H]3CC[C@@H](C2)N3)cc(-c2ccccc2)c1', 
-        'Brc1ccc(N2CCN(CCCCOc3ccc4ccccc4c3)CC2)cc1', 
+    assert result == ['Brc1cc(O[C@@H]2C[C@H]3CC[C@@H](C2)N3)cc(-c2ccccc2)c1',
+        'Brc1ccc(N2CCN(CCCCOc3ccc4ccccc4c3)CC2)cc1',
         'Brc1ccc(N2CCN(CCCN3CCC(Cc4ccccc4)CC3)CC2)cc1']
 
     result = su.rdkit_smiles_from_smiles(test_smiles, useCanonicalTautomers=False)
     assert result == ['Brc1cc(O[C@@H]2C[C@H]3CC[C@@H](C2)N3)cc(-c2ccccc2)c1',
-        'Brc1ccc(N2CCN(CCCCOc3ccc4ccccc4c3)CC2)cc1', 
+        'Brc1ccc(N2CCN(CCCCOc3ccc4ccccc4c3)CC2)cc1',
         'Brc1ccc(N2CCN(CCCN3CCC(Cc4ccccc4)CC3)CC2)cc1']
 
     result = su.rdkit_smiles_from_smiles(test_smiles, useIsomericSmiles=False)
@@ -157,7 +157,7 @@ def test_mol_wt_from_smiles():
 
 def test_canonical_tautomers_from_smiles():
     canonical_tautomers = [su.canonical_tautomers_from_smiles(s) for s in ['asdf']+test_smiles]
-    assert canonical_tautomers==[[''], 
+    assert canonical_tautomers==[[''],
         ['Brc1cc(O[C@@H]2C[C@H]3CC[C@@H](C2)N3)cc(-c2ccccc2)c1'],
         ['Brc1ccc(N2CCN(CCCCOc3ccc4ccccc4c3)CC2)cc1'],
         ['Brc1ccc(N2CCN(CCCN3CCC(Cc4ccccc4)CC3)CC2)cc1']]
@@ -177,4 +177,4 @@ def test_canonical_tautomers_from_smiles():
     test_draw_structure()
     test_smiles_to_inchi_key()
     test_mol_wt_from_smiles()
-    test_canonical_tautomers_from_smiles()
\ No newline at end of file
+    test_canonical_tautomers_from_smiles()
diff --git a/atomsci/ddm/test/unit/test_transformers.py b/atomsci/ddm/test/unit/test_transformers.py
index 69cfebd6..d3058c42 100644
--- a/atomsci/ddm/test/unit/test_transformers.py
+++ b/atomsci/ddm/test/unit/test_transformers.py
@@ -1,7 +1,61 @@
 import atomsci.ddm.pipeline.transformations as trans
+import atomsci.ddm.pipeline.parameter_parser as pp
 import numpy as np
+import pandas as pd
 from deepchem.data import NumpyDataset
+import pytest
 
+from sklearn.preprocessing import RobustScaler, PowerTransformer
+
+def test_sklearn_pipeline_wrapper():
+    """
+    Creates a mock dataset.
+        Tests the SklearnTransformerWrapper with RobustScaler on X.
+        Tests the SklearnTransformerWrapper with PowerTransformer on y.
+        Tests the SklearnTransformerWrapper with RobustScaler on w.
+        Asserts that the transformed values match the expected values.
+    """
+    # Create a mock dataset
+    X = np.array([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]])
+    y = np.array([[1.0], [3.0], [5.0]])
+    w = np.array([[1.0], [1.0], [1.0]])
+    ids = np.array(range(len(y)))
+    dataset = NumpyDataset(X=X, y=y, w=w, ids=ids)
+
+    # Test with RobustScaler on X
+    scaler = RobustScaler()
+    transformer = trans.SklearnPipelineWrapper(dataset, scaler, transform_X=True)
+    transformed_dataset = transformer.transform(dataset)
+    expected_transformed_X = scaler.fit_transform(X)
+    np.testing.assert_array_almost_equal(transformed_dataset.X, expected_transformed_X)
+
+    # Test untransform on X
+    with pytest.raises(NotImplementedError) as exception_info:
+        untransformed_X = transformer.untransform(transformed_dataset.X)
+    assert str(exception_info.value) == 'SklearnPipelineWrapper does not support inverse transforms'
+
+    # Test with PowerTransformer on y
+    power_transformer = PowerTransformer()
+    transformer = trans.SklearnPipelineWrapper(dataset, power_transformer, transform_y=True)
+    transformed_dataset = transformer.transform(dataset)
+    expected_transformed_y = power_transformer.fit_transform(y)
+    np.testing.assert_array_almost_equal(transformed_dataset.y, expected_transformed_y)
+
+    # Test untransform on y
+    with pytest.raises(NotImplementedError) as exception_info:
+        untransformed_y = transformer.untransform(transformed_dataset.y)
+    assert str(exception_info.value) == 'SklearnPipelineWrapper does not support inverse transforms'
+
+    # Test with RobustScaler on w
+    transformer = trans.SklearnPipelineWrapper(dataset, scaler, transform_w=True)
+    transformed_dataset = transformer.transform(dataset)
+    expected_transformed_w = scaler.fit_transform(w)
+    np.testing.assert_array_almost_equal(transformed_dataset.w, expected_transformed_w)
+
+    # Test untransform on w
+    with pytest.raises(NotImplementedError) as exception_info:
+        untransformed_w = transformer.untransform(transformed_dataset.w)
+    assert str(exception_info.value) == 'SklearnPipelineWrapper does not support inverse transforms'
 
 def test_no_missing_values():
     """
@@ -162,3 +216,87 @@ def test_normalization_transformer_missing_data_transform_X():
     expected_transformed_X = (X - expected_X_means) / expected_X_stds
     np.testing.assert_array_almost_equal(transformed_dataset.X, expected_transformed_X, decimal=6)
 
+def test_create_feature_transformers():
+    """Test the `create_feature_transformers` when params.transformers is None."""
+
+    params = pp.wrapper({})
+    params.transformers = None
+    transformers_x = trans.create_feature_transformers(
+        params,
+        featurization = None,
+        train_dset = None
+    )
+
+    assert transformers_x == []
+
+def test_zero_out_inf_nan_numpy_with_nonfinite_replaces_and_copies():
+    x = np.array([1.0, np.nan, np.inf, -np.inf, 2.5], dtype=float)
+    y = trans.zero_out_inf_nan(x)
+
+    assert isinstance(y, np.ndarray)
+    np.testing.assert_array_equal(y, np.array([1.0, 0.0, 0.0, 0.0, 2.5], dtype=float))
+
+    # Ensure it is a copy and original not mutated
+    assert y is not x
+    np.testing.assert_array_equal(x, np.array([1.0, np.nan, np.inf, -np.inf, 2.5], dtype=float))
+
+
+def test_zero_out_inf_nan_numpy_without_nonfinite_no_change_but_copy():
+    x = np.array([1.0, 2.0, 3.0], dtype=float)
+    y = trans.zero_out_inf_nan(x)
+
+    assert isinstance(y, np.ndarray)
+    np.testing.assert_array_equal(y, x)
+    assert y is not x
+
+
+def test_zero_out_inf_nan_series_with_nonfinite_replaces_preserves_index_and_name():
+    s = pd.Series([1.0, np.nan, np.inf, -np.inf], index=["a", "b", "c", "d"], name="vals")
+    out = trans.zero_out_inf_nan(s)
+
+    assert isinstance(out, pd.Series)
+    assert out.name == "vals"
+    assert list(out.index) == ["a", "b", "c", "d"]
+    np.testing.assert_array_equal(out.values, np.array([1.0, 0.0, 0.0, 0.0], dtype=float))
+
+    # Original not mutated
+    assert np.isnan(s.loc["b"])
+    assert np.isposinf(s.loc["c"])
+    assert np.isneginf(s.loc["d"])
+
+
+def test_zero_out_inf_nan_series_without_nonfinite_no_change_values():
+    s = pd.Series([1.0, 2.0], index=[10, 20], name="ok")
+    out = trans.zero_out_inf_nan(s)
+
+    assert isinstance(out, pd.Series)
+    assert out.name == "ok"
+    assert list(out.index) == [10, 20]
+    np.testing.assert_array_equal(out.values, s.values)
+
+
+def test_zero_out_inf_nan_dataframe_with_nonfinite_replaces_preserves_index_and_columns():
+    df = pd.DataFrame(
+        {"c1": [1.0, np.nan], "c2": [np.inf, 4.0]},
+        index=["r1", "r2"],
+    )
+    out = trans.zero_out_inf_nan(df)
+
+    assert isinstance(out, pd.DataFrame)
+    assert list(out.index) == ["r1", "r2"]
+    assert list(out.columns) == ["c1", "c2"]
+    np.testing.assert_array_equal(out.values, np.array([[1.0, 0.0], [0.0, 4.0]], dtype=float))
+
+    # Original not mutated
+    assert np.isnan(df.loc["r2", "c1"])
+    assert np.isposinf(df.loc["r1", "c2"])
+
+
+def test_zero_out_inf_nan_dataframe_without_nonfinite_no_change_values():
+    df = pd.DataFrame({"a": [1.0, 2.0], "b": [3.0, 4.0]}, index=[0, 1])
+    out = trans.zero_out_inf_nan(df)
+
+    assert isinstance(out, pd.DataFrame)
+    assert list(out.index) == [0, 1]
+    assert list(out.columns) == ["a", "b"]
+    np.testing.assert_array_equal(out.values, df.values)
\ No newline at end of file
diff --git a/atomsci/ddm/test/unit/utils_testing.py b/atomsci/ddm/test/unit/utils_testing.py
index 84c29c8b..7f538288 100644
--- a/atomsci/ddm/test/unit/utils_testing.py
+++ b/atomsci/ddm/test/unit/utils_testing.py
@@ -20,10 +20,14 @@
 def clean():
     split_files = glob.glob('./*_train_valid_test_*')
     for f in split_files:
-        os.remove(f)
+        try:
+            os.remove(f)
+        except FileNotFoundError:
+            # Another worker may have already removed this file.
+            pass
 
     if os.path.exists('pytest'):
-        shutil.rmtree('pytest')
+        shutil.rmtree('pytest', ignore_errors=True)
 
 def datastore_status():
     """Returns True if the datastore is down, False otherwise. Will figure out how to do this programmatically"""
diff --git a/atomsci/ddm/utils/curate_data.py b/atomsci/ddm/utils/curate_data.py
index 130adff6..3b9bc57c 100644
--- a/atomsci/ddm/utils/curate_data.py
+++ b/atomsci/ddm/utils/curate_data.py
@@ -519,10 +519,16 @@ def remove_outlier_replicates(df, response_col='pIC50', id_col='compound_id', ma
         max_diff_from_median (float): Maximum absolute difference from median value allowed for retained replicates.
 
     Returns:
-        result_df (DataFrame): Filtered data frame with outlier replicates removed.
+        result_df (DataFrame): Filtered data frame with outlier replicates removed. Rows with NaN values in the 
+            response column are removed as a preprocessing step before outlier detection.
 
     """
-
+    
+    prev_len = len(df)
+    df = df.dropna(subset=[response_col])
+    if prev_len != len(df):
+        print(f"Removed {prev_len - len(df)} rows with missing {response_col} values")
+    
     fr_df = freq_table(df, id_col, min_freq=2)
     rep_ids = fr_df[id_col].values.tolist()
     has_rep_df = df[df[id_col].isin(rep_ids)]
diff --git a/atomsci/ddm/utils/generate_transformers.py b/atomsci/ddm/utils/generate_transformers.py
new file mode 100644
index 00000000..7992c66e
--- /dev/null
+++ b/atomsci/ddm/utils/generate_transformers.py
@@ -0,0 +1,235 @@
+import pickle
+import glob
+import os
+import pandas as pd
+import atomsci.ddm.pipeline.featurization as feat
+import atomsci.ddm.pipeline.parameter_parser as pp
+import atomsci.ddm.pipeline.model_datasets as model_datasets
+import atomsci.ddm.pipeline.transformations as trans
+import atomsci.ddm.utils.struct_utils as struct_utils
+from deepchem.data import NumpyDataset
+import numpy as np
+import shutil
+import sklearn.utils as sku
+import logging
+logging.basicConfig(format='%(asctime)-15s %(message)s')
+log = logging.getLogger('ATOM')
+
+
+def prepare_csv_and_descriptor_with_dummy_response(csv_path, descriptor_type, temp_root, split_uuid='split_uuid'):
+    """
+    Copies the csv file and its descriptor file to a temp directory, preserving structure,
+    and adds a 'dummy_response' column of zeros to both.
+
+    Args:
+        csv_path (str): Path to the original CSV file.
+        descriptor_type (str): Descriptor type to look for in the descriptor file name.
+        temp_root (str): Root of the temporary directory to copy files into.
+        split_uuid (str): Unique identifier for the split file to look for and copy if it exists.
+
+    Returns:
+        (str, str): Paths to the new CSV and descriptor files in the temp directory.
+    """
+    # Find descriptor file
+    csv_dir = os.path.dirname(csv_path)
+    csv_base = os.path.splitext(os.path.basename(csv_path))[0]
+    descriptor_dir = os.path.join(csv_dir, 'scaled_descriptors')
+    descriptor_pattern = f"{csv_base}_with_{descriptor_type}_descriptors.csv"
+    descriptor_path = os.path.join(descriptor_dir, descriptor_pattern)
+    copy_descriptor_csv =  os.path.exists(descriptor_path)
+
+    # Find split file if it exists
+    split_pattern = os.path.join(csv_dir, f'{csv_base}_*_{split_uuid}.csv')
+    split_files = glob.glob(split_pattern)
+    if len(split_files)>1:
+        raise RuntimeError(f'Multiple splits found {split_files}')
+    if len(split_files)>0 and len(split_uuid)>0:
+        split_csv = split_files[0]
+        split_base = os.path.basename(split_csv)
+        temp_split_csv = os.path.join(temp_root, split_base)
+        shutil.copy(split_csv, temp_split_csv)
+
+    # Prepare destination paths
+    temp_csv_path = os.path.join(temp_root, os.path.basename(csv_path))
+    temp_descriptor_dir = os.path.join(temp_root, 'scaled_descriptors')
+    os.makedirs(temp_descriptor_dir, exist_ok=True)
+    temp_descriptor_path = os.path.join(temp_descriptor_dir, os.path.basename(descriptor_path))
+
+    # Copy and add dummy_response to CSV
+    df_csv = pd.read_csv(csv_path)
+    df_csv['dummy_response'] = 0
+    df_csv.to_csv(temp_csv_path, index=False)
+
+    # Copy and add dummy_response to descriptor file
+    if copy_descriptor_csv:
+        df_desc = pd.read_csv(descriptor_path)
+        df_desc['dummy_response'] = 0
+        df_desc.to_csv(temp_descriptor_path, index=False)
+
+    return temp_csv_path
+
+def load_all_datasets(
+    transformer_dataset_key_configs,
+    featurizer,
+    descriptor_type
+):
+    """Loads datasets from configs and builds NumpyDataset
+
+    Args:
+        csvs_or_tuples (list): List of csv file paths or (csv_file, split_uuid) tuples.
+        featurizer (str): The featurizer type (e.g., 'ecfp', 'graphconv', 'computed_descriptors', etc.).
+        descriptor_type (str): Descriptor type (e.g., 'moe', 'rdkit_raw', etc.).
+
+    Returns:
+        NumpyDataset
+    """
+    featurized_datasets = []
+    for ds_config in transformer_dataset_key_configs:
+        # Prepare params for this dataset
+        params_dict = dict()
+        params_dict.update(ds_config)
+        params_dict['featurizer'] = featurizer
+        params_dict['descriptor_type'] = descriptor_type
+        params_dict['feature_transform_type'] = 'Identity'
+        
+        # check if there is a split_uuid in the config
+        split_uuid = params_dict.get('split_uuid', None)
+
+        params = pp.wrapper(params_dict)
+        dataset = model_datasets.create_and_load_model_dataset(params, ds_client=None)
+
+        # If split_uuid is provided, use only the training subset
+        if split_uuid:
+            dataset.split_dataset()
+            train_dset = dataset.train_valid_dsets[0][0]
+            featurized_datasets.append(train_dset)
+        else:
+            # this is a NumpyDataset with all data
+            featurized_datasets.append(dataset.dataset)
+        
+    # Combine all dataframes for fitting transformers
+    combined_dataset = NumpyDataset(
+        X=np.vstack([d.X for d in featurized_datasets]),
+        y=np.vstack([d.y for d in featurized_datasets]),
+        ids=np.concatenate([d.ids for d in featurized_datasets]),
+        w=np.concatenate([d.w for d in featurized_datasets])
+    )
+
+    return combined_dataset
+
+def filter_outlier_features(dataset_key, id_col, smiles_col, response_cols, featurizer, descriptor_type, threshold=1e10):
+    """Looks for compounds with very large descriptor values.
+
+    Args:
+        dataset_key_configs (list): List of dataset key configuration dictionaries.
+        featurizer (str): The featurizer type (e.g., 'ecfp', 'graphconv', 'computed_descriptors', etc.).
+        descriptor_type (str): Descriptor type (e.g., 'moe', 'rdkit_raw', etc.).
+        threshold (float): Threshold for filtering large descriptor values.
+
+    Returns:
+        DataFrame with outlier compound_ids, descriptor column names, and descriptor values that exceed the threshold.
+    """
+    dataset_key_config = {
+        'dataset_key': dataset_key,
+        'id_col': id_col,
+        'smiles_col': smiles_col,
+        'response_cols': response_cols
+    }
+    params_dict = dict()
+    params_dict.update(dataset_key_config)
+    params_dict['featurizer'] = featurizer
+    params_dict['descriptor_type'] = descriptor_type
+    params_dict['feature_transform_type'] = 'Identity'
+    params_dict['verbose'] = True
+    
+    params = pp.wrapper(params_dict)
+    dataset = model_datasets.create_and_load_model_dataset(params, ds_client=None)
+
+    abs_X = np.abs(dataset.dataset.X)
+    try:
+        sku.assert_all_finite(abs_X)
+    except ValueError:
+        # data contains inf or nan values
+        log.warning("SklearnPipelineWrapper: data contains NaN or Inf; replacing with zeros")
+        abs_X = trans.zero_out_inf_nan(abs_X)
+   
+
+    large_values = np.argwhere(abs_X > threshold)
+    feature_cols = np.array(dataset.featurization.get_feature_columns())
+
+    return dataset.dataset.ids[large_values[:,0]], [feature_cols[i] for i in large_values[:,1]], [abs_X[i,j] for i,j in large_values]
+
+def filter_outlier_MW(dataset_key, smiles_col, threshold=1000, workers=8):
+    """Filters datasets and looks for compounds with very large molecular weights.
+
+    Args:
+        dataset_key (str): Path to the dataset CSV file.
+        smiles_col (str): Name of the column containing SMILES strings.
+        threshold (float): Threshold for filtering large molecular weights.
+        workers (int): Number of workers to use for parallel processing in calculating molecular weights.
+        workers (int): Number of workers to use for parallel processing in calculating molecular weights.
+
+    Returns:
+        List of SMILES with molecular weights that exceed the threshold.
+
+    """
+    # molecular weight > 1000
+    # calculate molecular weight here.
+    dataset_df = pd.read_csv(dataset_key)
+    mol_weights = struct_utils.mol_wt_from_smiles(dataset_df[smiles_col].to_list(), workers=workers)
+    mw_large = np.argwhere(np.array(mol_weights) > threshold)
+
+    return dataset_df[smiles_col].iloc[mw_large.flatten()].tolist()
+
+def build_and_save_feature_transformers_from_csvs(
+    transformer_dataset_key_configs,
+    dest_pkl_path,
+    featurizer,
+    descriptor_type,
+    feature_transform_type,
+    **kwargs
+):
+    """
+    Build feature transformers_x from a list of csv files (or csv+split_uuid tuples) and save them as a pickle file,
+    including the params object used to create them. This function saves feature transformers suitable for use
+    for one fold. 
+
+    Args:
+        transformer_dataset_key_configs (list): A list of dictionaries that contain information about each dataset_key such
+        as id_col, smiles_col, response_cols, split_uuids, etc.
+        dest_pkl_path (str): Path to save the pickle file.
+        featurizer (str): The featurizer type (e.g., 'ecfp', 'graphconv', 'computed_descriptors', etc.).
+        descriptor_type (str): Descriptor type (e.g., 'moe', 'rdkit_raw', etc.).
+        feature_transform_type (str): The type of transformer to use (e.g., 'RobustScaler', 'PowerTransformer', etc.).
+        **kwargs: Additional keyword arguments for transformer params.
+
+    Returns:
+        None
+    """
+    combined_dataset = load_all_datasets(transformer_dataset_key_configs=transformer_dataset_key_configs, featurizer=featurizer, descriptor_type=descriptor_type)
+
+    # Build a single params object for transformer fitting
+    params_dict = dict(
+        featurizer=featurizer,
+        feature_transform_type=feature_transform_type,
+        descriptor_type=descriptor_type,
+        **kwargs
+    )
+    params = pp.wrapper(params_dict)
+    featurization = feat.create_featurization(params)
+
+    # Build the feature transformers (transformers_x)
+    transformers_x = featurization.create_feature_transformer(combined_dataset, params)
+
+    # Save both transformers_x and params to a pickle file
+    # copy processed params back into params_dict in case wrapper
+    # updates them.
+    for k in params_dict.keys():
+        if k in params.__dict__:
+            params_dict[k] = params.__dict__[k]
+
+    params_dict['transformer_dataset_key_configs'] = transformer_dataset_key_configs
+    with open(dest_pkl_path, 'wb') as f:
+        pickle.dump({'transformers_x': transformers_x, 'params': params_dict}, f)
+
+    print(f"Feature transformers_x and params saved to {dest_pkl_path}")
\ No newline at end of file
diff --git a/atomsci/ddm/utils/hyperparam_search_wrapper.py b/atomsci/ddm/utils/hyperparam_search_wrapper.py
index 5e1d999e..1dbfc894 100755
--- a/atomsci/ddm/utils/hyperparam_search_wrapper.py
+++ b/atomsci/ddm/utils/hyperparam_search_wrapper.py
@@ -25,8 +25,9 @@
 import traceback
 import copy
 import pickle
+import math
 
-from hyperopt import fmin, tpe, hp, Trials, STATUS_OK
+import optuna
 
 from atomsci.ddm.pipeline import featurization as feat
 from atomsci.ddm.pipeline import model_pipeline as mp
@@ -34,8 +35,9 @@
 from atomsci.ddm.pipeline import model_datasets as model_datasets
 from atomsci.ddm.utils import datastore_functions as dsf
 from atomsci.ddm.pipeline import model_tracker as trkr
-logging.basicConfig(format='%(asctime)-15s %(message)s')
 
+logging.basicConfig(format='%(asctime)-15s %(message)s')
+optuna.logging.set_verbosity(optuna.logging.WARNING)
 
 
 def run_command(shell_script, python_path, script_dir, params):
@@ -135,8 +137,6 @@ def reformat_filter_dict(filter_dict):
                'training_dataset':
                        {'bucket', 'dataset_key', 'dataset_oid', 'num_classes','feature_transform_type',
                         'response_transform_type', 'id_col', 'smiles_col', 'response_cols'},
-               'umap_specific':
-                       {'umap_dim', 'umap_metric', 'umap_min_dist', 'umap_neighbors','umap_targ_wt'}
               }
     if filter_dict['model_type'] == 'NN':
         rename_dict['nn_specific'] = {'baseline_epoch', 'batch_size', 'best_epoch', 'bias_init_consts','dropouts',
@@ -270,7 +270,6 @@ def __init__(self, params):
         self.hyperparam_layers = {'layer_sizes', 'dropouts', 'weight_init_stddevs', 'bias_init_consts'}
         self.hyperparam_keys = {'model_type', 'featurizer', 'splitter', 'learning_rate', 'weight_decay_penalty',
                                 'rf_estimators', 'rf_max_features', 'rf_max_depth',
-                                'umap_dim', 'umap_targ_wt', 'umap_metric', 'umap_neighbors', 'umap_min_dist',
                                 'xgb_learning_rate',
                                 'xgb_gamma'}
         self.nn_specific_keys = {'learning_rate', 'layers','weight_decay_penalty'}
@@ -1291,27 +1290,51 @@ def generate_combo(self, params_dict):
                 new_dict[key] = value
         return new_dict
 
-def build_hyperopt_search_domain(label, method, param_list):
-    """Generate HyperOpt search domain object from method and parameters, layer_nums is only for NN models.
-    This function is used by the HyperOptSearch class, not intended for standalone usage.
+def build_optuna_suggest(trial, label, method, param_list):
+    """Sample a hyperparameter value from an Optuna trial using the specified distribution.
+
+    Translates the same ``method|params`` format used across AMPL config files into the
+    corresponding ``trial.suggest_*`` call.  This function is used by the OptunaSearch class
+    and is not intended for standalone usage.
+
+    Note: ``loguniform`` param_list values must be in natural-log scale (matching the legacy
+    hyperopt convention), e.g. ``[-13.8, -6.9]``.  They are converted to actual-scale bounds
+    via ``math.exp()`` before being passed to Optuna.
+
+    Args:
+        trial: An :class:`optuna.trial.Trial` object.
+        label (str): Name of the hyperparameter (used as the Optuna parameter name).
+        method (str): Sampling method — one of ``'choice'``, ``'uniform'``,
+            ``'loguniform'``, or ``'uniformint'``.
+        param_list (list): Parameters for the chosen method:
+            - ``choice``: list of candidate values.
+            - ``uniform``/``loguniform``/``uniformint``: ``[low, high]``.
+
+    Returns:
+        The sampled value.
+
+    Raises:
+        ValueError: If *method* is not one of the supported options.
     """
     if method == "choice":
-        return hp.choice(label, param_list)
+        return trial.suggest_categorical(label, param_list)
     elif method == "uniform":
-        return hp.uniform(label, param_list[0], param_list[1])
+        return trial.suggest_float(label, param_list[0], param_list[1])
     elif method == "loguniform":
-        return hp.loguniform(label, param_list[0], param_list[1])
+        # param_list values are in natural-log scale (legacy hyperopt convention);
+        # Optuna expects actual-scale bounds with log=True.
+        return trial.suggest_float(label, math.exp(param_list[0]), math.exp(param_list[1]), log=True)
     elif method == "uniformint":
-        return hp.uniformint(label, param_list[0], param_list[1])
+        return trial.suggest_int(label, int(param_list[0]), int(param_list[1]))
     else:
-        raise Exception(f"Method {method} is not supported, choose from 'choice, uniform, loguniform, uniformint'.")
+        raise ValueError(f"Method {method} is not supported, choose from 'choice, uniform, loguniform, uniformint'.")
 
-class HyperOptSearch():
-    """Perform hyperparameter search with Bayesian Optmization (Tree Parzen Estimator)
+class OptunaSearch():
+    """Perform hyperparameter search with Bayesian Optimization (Tree Parzen Estimator) using Optuna.
 
-    To use HyperOptSearch, modify the config json file as follows:
+    To use OptunaSearch, modify the config json file as follows:
 
-        serach_type: use "hyperopt"
+        search_type: use "hyperopt"
 
         result_dir: use two directories (recommended), separated by comma, 1st one will be used to save the best model tarball, 2nd one will be used to store all models during the process.  e.g. "result_dir": "/path/of/the/final/dir,/path/of/the/temp/dir"
 
@@ -1320,7 +1343,7 @@ class HyperOptSearch():
         lr: specify learning rate searching method and related parameters as the following scheme.
             method|parameter1,parameter2...
 
-            method: supported searching schemes in HyperOpt include: choice, uniform, loguniform, and uniformint, see https://github.com/hyperopt/hyperopt/wiki/FMin for details.
+            method: supported searching schemes include: choice, uniform, loguniform, and uniformint. See Optuna documentation for details.
 
     parameters:
         choice: all values to search from, separated by comma, e.g. choice|0.0001,0.0005,0.0002,0.001
@@ -1365,136 +1388,9 @@ def __init__(self, params):
         else:
             self.max_eval = 100
 
-        #define the searching space
-        self.space = {}
-        if isinstance(self.params.featurizer, list):
-            self.space["featurizer"] = build_hyperopt_search_domain("featurizer", "choice", self.params.featurizer)
-        if isinstance(self.params.descriptor_type, list):
-            self.space["descriptor_type"] = build_hyperopt_search_domain("descriptor_type", "choice", self.params.descriptor_type)
-        if self.params.model_type == "RF":
-            #build searching domain for RF parameters
-            if self.params.rfe:
-                domain_list = self.params.rfe.split("|")
-                method = domain_list[0]
-                par_list = [float(e) for e in domain_list[1].split(",")]
-                self.space["rf_estimators"] = build_hyperopt_search_domain("rf_estimators", method, par_list)
-
-            if self.params.rfd:
-                domain_list = self.params.rfd.split("|")
-                method = domain_list[0]
-                par_list = [float(e) for e in domain_list[1].split(",")]
-                self.space["rf_max_depth"] = build_hyperopt_search_domain("rf_max_depth", method, par_list)
-
-            if self.params.rff:
-                domain_list = self.params.rff.split("|")
-                method = domain_list[0]
-                par_list = [float(e) for e in domain_list[1].split(",")]
-                self.space["rf_max_features"] = build_hyperopt_search_domain("rf_max_features", method, par_list)
-        elif self.params.model_type == "NN":
-            #build searching domain for NN parameters
-            if self.params.lr:
-                domain_list = self.params.lr.split("|")
-                method = domain_list[0]
-                par_list = [float(e) for e in domain_list[1].split(",")]
-                self.space["learning_rate"] = build_hyperopt_search_domain("learning_rate", method, par_list)
-
-            # for layer sizes, use a different method if the ls_ratio is provided
-            if self.params.ls:
-                domain_list = self.params.ls.split("|")
-                method = domain_list[0]
-                num_layer = int(domain_list[1])
-                par_list = [float(e) for e in domain_list[2].split(",")]
-                if not self.params.ls_ratio:
-                    for i in range(num_layer):
-                        self.space[f"ls{i}"] = build_hyperopt_search_domain(f"ls{i}", method, par_list)
-                else:
-                    self.space["ls"] = build_hyperopt_search_domain("ls", method, par_list)
-                    domain_list = self.params.ls_ratio.split("|")
-                    method = domain_list[0]
-                    par_list = [float(e) for e in domain_list[-1].split(",")]
-                    for i in range(1,num_layer):
-                        self.space[f"ratio{i}"] = build_hyperopt_search_domain(f"ratio{i}", method, par_list)
-
-            # dropouts
-            if self.params.dp:
-                domain_list = self.params.dp.split("|")
-                method = domain_list[0]
-                num_layer = int(domain_list[1])
-                par_list = [float(e) for e in domain_list[2].split(",")]
-                for i in range(num_layer):
-                    self.space[f"dp{i}"] = build_hyperopt_search_domain(f"dp{i}", method, par_list)
-
-            # weight decay penalty
-            if self.params.wdp:
-                domain_list = self.params.wdp.split("|")
-                method = domain_list[0]
-                par_list = [float(e) for e in domain_list[1].split(",")]
-                self.space["weight_decay_penalty"] = build_hyperopt_search_domain("weight_decay_penalty", method, par_list)
-
-            # weight decay penalty type
-            if self.params.wdt:
-                domain_list = self.params.wdt.split("|")
-                method = domain_list[0]
-                par_list = domain_list[1].split(",")
-                self.space["weight_decay_penalty_type"] = build_hyperopt_search_domain("weight_decay_penalty_type", method, par_list)
-
-        elif self.params.model_type == "xgboost":
-            #build searching domain for XGBoost parameters
-            if self.params.xgbg:
-                domain_list = self.params.xgbg.split("|")
-                method = domain_list[0]
-                par_list = [float(e) for e in domain_list[1].split(",")]
-                self.space["xgbg"] = build_hyperopt_search_domain("xgbg", method, par_list)
-
-            if self.params.xgba:
-                domain_list = self.params.xgba.split("|")
-                method = domain_list[0]
-                par_list = [float(e) for e in domain_list[1].split(",")]
-                self.space["xgba"] = build_hyperopt_search_domain("xgba", method, par_list)
-
-            if self.params.xgbb:
-                domain_list = self.params.xgbb.split("|")
-                method = domain_list[0]
-                par_list = [float(e) for e in domain_list[1].split(",")]
-                self.space["xgbb"] = build_hyperopt_search_domain("xgbb", method, par_list)
-
-            if self.params.xgbl:
-                domain_list = self.params.xgbl.split("|")
-                method = domain_list[0]
-                par_list = [float(e) for e in domain_list[1].split(",")]
-                self.space["xgbl"] = build_hyperopt_search_domain("xgbl", method, par_list)
-
-            if self.params.xgbd:
-                domain_list = self.params.xgbd.split("|")
-                method = domain_list[0]
-                par_list = [float(e) for e in domain_list[1].split(",")]
-                self.space["xgbd"] = build_hyperopt_search_domain("xgbd", method, par_list)
-
-            if self.params.xgbc:
-                domain_list = self.params.xgbc.split("|")
-                method = domain_list[0]
-                par_list = [float(e) for e in domain_list[1].split(",")]
-                self.space["xgbc"] = build_hyperopt_search_domain("xgbc", method, par_list)
-
-            if self.params.xgbs:
-                domain_list = self.params.xgbs.split("|")
-                method = domain_list[0]
-                par_list = [float(e) for e in domain_list[1].split(",")]
-                self.space["xgbs"] = build_hyperopt_search_domain("xgbs", method, par_list)
-
-            if self.params.xgbn:
-                domain_list = self.params.xgbn.split("|")
-                method = domain_list[0]
-                par_list = [float(e) for e in domain_list[1].split(",")]
-                self.space["xgbn"] = build_hyperopt_search_domain("xgbn", method, par_list)
-
-            if self.params.xgbw:
-                domain_list = self.params.xgbw.split("|")
-                method = domain_list[0]
-                par_list = [float(e) for e in domain_list[1].split(",")]
-                self.space["xgbw"] = build_hyperopt_search_domain("xgbw", method, par_list)
-
-        self.log = logging.getLogger("hyperopt_search")
+        # The search space is now built inside the objective() function at call time,
+        # using trial.suggest_* calls rather than a pre-built space dictionary.
+        self.log = logging.getLogger("optuna_search")
 
 
     def run_search(self):
@@ -1506,16 +1402,80 @@ def run_search(self):
         else:
             fd = f"{feat}_{desc}"
 
-        def lossfn(p):
+        def objective(trial):
+            # Build a local param dict by sampling from the trial using build_optuna_suggest.
+            # This mirrors the old hyperopt approach where fmin populated a dict p for lossfn.
+            p = {}
+            if isinstance(self.params.featurizer, list):
+                p["featurizer"] = trial.suggest_categorical("featurizer", self.params.featurizer)
+            if isinstance(self.params.descriptor_type, list):
+                p["descriptor_type"] = trial.suggest_categorical("descriptor_type", self.params.descriptor_type)
+
+            if self.params.model_type == "RF":
+                if self.params.rfe:
+                    dl = self.params.rfe.split("|")
+                    p["rf_estimators"] = build_optuna_suggest(trial, "rf_estimators", dl[0], [float(e) for e in dl[1].split(",")])
+                if self.params.rfd:
+                    dl = self.params.rfd.split("|")
+                    p["rf_max_depth"] = build_optuna_suggest(trial, "rf_max_depth", dl[0], [float(e) for e in dl[1].split(",")])
+                if self.params.rff:
+                    dl = self.params.rff.split("|")
+                    p["rf_max_features"] = build_optuna_suggest(trial, "rf_max_features", dl[0], [float(e) for e in dl[1].split(",")])
+            elif self.params.model_type == "NN":
+                if self.params.lr:
+                    dl = self.params.lr.split("|")
+                    p["learning_rate"] = build_optuna_suggest(trial, "learning_rate", dl[0], [float(e) for e in dl[1].split(",")])
+                # for layer sizes, use a different method if the ls_ratio is provided
+                if self.params.ls:
+                    dl = self.params.ls.split("|")
+                    ls_method = dl[0]
+                    num_layer = int(dl[1])
+                    ls_par_list = [float(e) for e in dl[2].split(",")]
+                    if not self.params.ls_ratio:
+                        for i in range(num_layer):
+                            p[f"ls{i}"] = build_optuna_suggest(trial, f"ls{i}", ls_method, ls_par_list)
+                    else:
+                        p["ls"] = build_optuna_suggest(trial, "ls", ls_method, ls_par_list)
+                        ratio_dl = self.params.ls_ratio.split("|")
+                        ratio_par_list = [float(e) for e in ratio_dl[-1].split(",")]
+                        for i in range(1, num_layer):
+                            p[f"ratio{i}"] = build_optuna_suggest(trial, f"ratio{i}", ratio_dl[0], ratio_par_list)
+                # dropouts
+                if self.params.dp:
+                    dl = self.params.dp.split("|")
+                    dp_method = dl[0]
+                    num_layer = int(dl[1])
+                    dp_par_list = [float(e) for e in dl[2].split(",")]
+                    for i in range(num_layer):
+                        p[f"dp{i}"] = build_optuna_suggest(trial, f"dp{i}", dp_method, dp_par_list)
+                # weight decay penalty
+                if self.params.wdp:
+                    dl = self.params.wdp.split("|")
+                    p["weight_decay_penalty"] = build_optuna_suggest(trial, "weight_decay_penalty", dl[0], [float(e) for e in dl[1].split(",")])
+                # weight decay penalty type
+                if self.params.wdt:
+                    dl = self.params.wdt.split("|")
+                    p["weight_decay_penalty_type"] = build_optuna_suggest(trial, "weight_decay_penalty_type", dl[0], dl[1].split(","))
+            elif self.params.model_type == "xgboost":
+                xgb_map = [
+                    ("xgbg", "xgbg"), ("xgba", "xgba"), ("xgbb", "xgbb"), ("xgbl", "xgbl"),
+                    ("xgbd", "xgbd"), ("xgbc", "xgbc"), ("xgbs", "xgbs"), ("xgbn", "xgbn"), ("xgbw", "xgbw"),
+                ]
+                for short, label in xgb_map:
+                    val_str = getattr(self.params, short, None)
+                    if val_str:
+                        dl = val_str.split("|")
+                        p[label] = build_optuna_suggest(trial, label, dl[0], [float(e) for e in dl[1].split(",")])
+
+            # Assign sampled values back to self.params — logic identical to the old lossfn(p)
             if "featurizer" in p:
                 self.params.featurizer = p["featurizer"]
-
             if "descriptor_type" in p:
                 self.params.descriptor_type = p["descriptor_type"]
 
             if self.params.model_type == "RF":
                 if self.params.rfe:
-                    self.params.rf_estimators =  p["rf_estimators"]
+                    self.params.rf_estimators = p["rf_estimators"]
                 if self.params.rfd:
                     self.params.rf_max_depth = p["rf_max_depth"]
                 if self.params.rff:
@@ -1536,7 +1496,7 @@ def lossfn(p):
                         self.params.layer_sizes = ",".join([str(p[e]) for e in p if e[:2] == "ls"])
                     else:
                         list_layer_sizes = [p["ls"]]
-                        for i in range(1,len([e for e in p if e[:5] == "ratio"])+1):
+                        for i in range(1, len([e for e in p if e[:5] == "ratio"]) + 1):
                             list_layer_sizes.append(int(list_layer_sizes[-1] * p[f"ratio{i}"]))
                         self.params.layer_sizes = ",".join([str(e) for e in list_layer_sizes])
                 hp_params = f'{self.params.learning_rate}_{self.params.layer_sizes}_{self.params.dropouts}_{self.params.weight_decay_penalty_type}_{self.params.weight_decay_penalty}'
@@ -1589,7 +1549,7 @@ def lossfn(p):
 
             tparam = parse.wrapper(self.params.__dict__)
             print(f"{self.params.model_type} model with {self.params.featurizer} and {self.params.descriptor_type}")
-            # make sure classification model has uncertainty as False. 
+            # make sure classification model has uncertainty as False.
             if tparam.prediction_type != "regression":
                 tparam.uncertainty = False
             pl = mp.ModelPipeline(tparam)
@@ -1598,7 +1558,7 @@ def lossfn(p):
             try:
                 pl.train_model()
             except Exception:
-                self.log.exception('Exception found during hyperopt training')
+                self.log.exception('Exception found during Optuna training')
                 model_failed = True
 
             subsets = ["train", "valid", "test"]
@@ -1614,31 +1574,54 @@ def lossfn(p):
                         sub_pred_results = {"roc_auc_score": 0, "accuracy_score": 0}
 
                 if tparam.prediction_type == "regression":
-                    pred_results[subset]["r2"] = sub_pred_results['r2_score']
-                    pred_results[subset]["rms"] = sub_pred_results['rms_score']
+                    r2_score = sub_pred_results.get('r2_score', np.nan)
+                    rms_score = sub_pred_results.get('rms_score', np.nan)
+                    if not np.isfinite(r2_score):
+                        r2_score = 0
+                    if not np.isfinite(rms_score):
+                        rms_score = 100
+                    pred_results[subset]["r2"] = r2_score
+                    pred_results[subset]["rms"] = rms_score
                 else:
-                    pred_results[subset]["roc_auc"] = sub_pred_results["roc_auc_score"]
-                    pred_results[subset]["acc"] = sub_pred_results["accuracy_score"]
+                    roc_auc_score = sub_pred_results.get("roc_auc_score", np.nan)
+                    accuracy_score = sub_pred_results.get("accuracy_score", np.nan)
+                    if not np.isfinite(roc_auc_score):
+                        roc_auc_score = 0
+                    if not np.isfinite(accuracy_score):
+                        accuracy_score = 0
+                    pred_results[subset]["roc_auc"] = roc_auc_score
+                    pred_results[subset]["acc"] = accuracy_score
+
+            # Store per-trial extras as user attributes; return the scalar loss for Optuna.
+            trial.set_user_attr("model", tparam.model_tarball_path)
+            trial.set_user_attr("featurizer", tparam.featurizer)
+            trial.set_user_attr("desc", tparam.descriptor_type)
+            trial.set_user_attr("hp_params", hp_params)
             if tparam.prediction_type == "regression":
-                res_dict = {'loss': 1-pred_results["valid"]["r2"], 'status': STATUS_OK, 'model': tparam.model_tarball_path, 'featurizer': tparam.featurizer, 'desc': tparam.descriptor_type}
+                loss = 1 - pred_results["valid"]["r2"]
                 for subset in subsets:
-                    res_dict[f"{subset}_r2"] = pred_results[subset]["r2"]
-                    res_dict[f"{subset}_rms"] = pred_results[subset]["rms"]
+                    trial.set_user_attr(f"{subset}_r2", pred_results[subset]["r2"])
+                    trial.set_user_attr(f"{subset}_rms", pred_results[subset]["rms"])
             else:
-                res_dict = {'loss': 100-pred_results["valid"]["roc_auc"], 'status': STATUS_OK, 'model': tparam.model_tarball_path, 'featurizer': tparam.featurizer, 'desc': tparam.descriptor_type}
+                loss = 100 - pred_results["valid"]["roc_auc"]
                 for subset in subsets:
-                    res_dict[f"{subset}_roc_auc"] = pred_results[subset]["roc_auc"]
-                    res_dict[f"{subset}_acc"] = pred_results[subset]["acc"]
-            res_dict["hp_params"] = hp_params
+                    trial.set_user_attr(f"{subset}_roc_auc", pred_results[subset]["roc_auc"])
+                    trial.set_user_attr(f"{subset}_acc", pred_results[subset]["acc"])
 
             # print the model metrics as logs
             print()
             if tparam.prediction_type == "regression":
-                print(f'model_performance|{res_dict["train_r2"]:.3f}|{res_dict["train_rms"]:.3f}|{res_dict["valid_r2"]:.3f}|{res_dict["valid_rms"]:.3f}|{res_dict["test_r2"]:.3f}|{res_dict["test_rms"]:.3f}|{res_dict["hp_params"]}|{res_dict["model"]}\n')
+                print(f'model_performance|{pred_results["train"]["r2"]:.3f}|{pred_results["train"]["rms"]:.3f}'
+                      f'|{pred_results["valid"]["r2"]:.3f}|{pred_results["valid"]["rms"]:.3f}'
+                      f'|{pred_results["test"]["r2"]:.3f}|{pred_results["test"]["rms"]:.3f}'
+                      f'|{hp_params}|{tparam.model_tarball_path}\n')
             else:
-                print(f'model_performance|{res_dict["train_roc_auc"]:.3f}|{res_dict["train_acc"]:.3f}|{res_dict["valid_roc_auc"]:.3f}|{res_dict["valid_acc"]:.3f}|{res_dict["test_roc_auc"]:.3f}|{res_dict["test_acc"]:.3f}|{res_dict["hp_params"]}|{res_dict["model"]}\n')
+                print(f'model_performance|{pred_results["train"]["roc_auc"]:.3f}|{pred_results["train"]["acc"]:.3f}'
+                      f'|{pred_results["valid"]["roc_auc"]:.3f}|{pred_results["valid"]["acc"]:.3f}'
+                      f'|{pred_results["test"]["roc_auc"]:.3f}|{pred_results["test"]["acc"]:.3f}'
+                      f'|{hp_params}|{tparam.model_tarball_path}\n')
 
-            return res_dict
+            return loss
 
         if self.params.prediction_type == "regression":
             print('model_performance|train_r2|train_rms|valid_r2|valid_rms|test_r2|test_rms|model_params|model\n')
@@ -1646,58 +1629,57 @@ def lossfn(p):
             print('model_performance|train_roc_auc|train_acc|valid_roc_auc|valid_acc|test_roc_auc|test_acc|model_params|model\n')
 
         if self.params.hp_checkpoint_load is not None and os.path.isfile(self.params.hp_checkpoint_load):
-            print(f"load hpo trial object from {self.params.hp_checkpoint_load}")
+            print(f"load hpo study object from {self.params.hp_checkpoint_load}")
             with open(self.params.hp_checkpoint_load, "rb") as f:
-                trials = pickle.load(f)
+                study = pickle.load(f)
         else:
-            trials = Trials()
+            study = optuna.create_study(direction='minimize')
 
         if self.params.hp_checkpoint_save is not None:
             print("hp_checkpoint_save provided, save a checkpoint file every 5 trials.")
-            max_evals = 5
             while True:
                 if os.path.isfile(self.params.hp_checkpoint_save):
-                    print(f"load hpo trial object from {self.params.hp_checkpoint_save}")
+                    print(f"load hpo study object from {self.params.hp_checkpoint_save}")
                     with open(self.params.hp_checkpoint_save, "rb") as f:
-                        trials = pickle.load(f)
-                    max_evals = min(len(trials) + 5, self.max_eval)
-                else:
-                    max_evals = min(max_evals, self.max_eval)
+                        study = pickle.load(f)
+                n_done = len(study.trials)
+                n_new = min(5, self.max_eval - n_done)
 
-                _best = fmin(lossfn, self.space, algo=tpe.suggest, max_evals=max_evals, trials=trials)
+                study.optimize(objective, n_trials=n_new)
 
-                print(f"Save HPO trial object to {self.params.hp_checkpoint_save}")
+                print(f"Save HPO study object to {self.params.hp_checkpoint_save}")
                 with open(self.params.hp_checkpoint_save, "wb") as f:
-                    pickle.dump(trials, f)
+                    pickle.dump(study, f)
 
-                if max_evals == self.max_eval:
+                if len(study.trials) >= self.max_eval:
                     break
         else:
-            _best = fmin(lossfn, self.space, algo=tpe.suggest, max_evals=self.max_eval, trials=trials)
+            study.optimize(objective, n_trials=self.max_eval)
 
         print("Generating the performance -- iteration table and Copy the best model tarball.")
 
-        feat_list = [trials.trials[i]["result"]["featurizer"] for i in range(len(trials.trials))]
-        desc_list = [trials.trials[i]["result"]["desc"] for i in range(len(trials.trials))]
-        hp_params_list = [trials.trials[i]["result"]["hp_params"] for i in range(len(trials.trials))]
-        trial_data = {"trial": list(range(len(trials.trials))), "featurizer": feat_list, "descriptor": desc_list, "model_params": hp_params_list}
+        n_trials = len(study.trials)
+        feat_list = [study.trials[i].user_attrs["featurizer"] for i in range(n_trials)]
+        desc_list = [study.trials[i].user_attrs["desc"] for i in range(n_trials)]
+        hp_params_list = [study.trials[i].user_attrs["hp_params"] for i in range(n_trials)]
+        trial_data = {"trial": list(range(n_trials)), "featurizer": feat_list, "descriptor": desc_list, "model_params": hp_params_list}
         subsets = ["train", "valid", "test"]
         for subset in subsets:
             if self.params.prediction_type == "regression":
-                trial_data[f"{subset}_r2"] = [trials.trials[i]["result"][f"{subset}_r2"] for i in range(len(trials.trials))]
-                trial_data[f"{subset}_rms"] = [trials.trials[i]["result"][f"{subset}_rms"] for i in range(len(trials.trials))]
+                trial_data[f"{subset}_r2"] = [study.trials[i].user_attrs[f"{subset}_r2"] for i in range(n_trials)]
+                trial_data[f"{subset}_rms"] = [study.trials[i].user_attrs[f"{subset}_rms"] for i in range(n_trials)]
             else:
-                trial_data[f"{subset}_roc_auc"] = [trials.trials[i]["result"][f"{subset}_roc_auc"] for i in range(len(trials.trials))]
-                trial_data[f"{subset}_acc"] = [trials.trials[i]["result"][f"{subset}_acc"] for i in range(len(trials.trials))]
+                trial_data[f"{subset}_roc_auc"] = [study.trials[i].user_attrs[f"{subset}_roc_auc"] for i in range(n_trials)]
+                trial_data[f"{subset}_acc"] = [study.trials[i].user_attrs[f"{subset}_acc"] for i in range(n_trials)]
         perf = pd.DataFrame(trial_data)
 
         if self.params.prediction_type == "regression":
             best_trial = perf.sort_values(by="valid_r2", ascending=False)["trial"].iloc[0]
-            best_model = trials.trials[best_trial]["result"]["model"]
+            best_model = study.trials[best_trial].user_attrs["model"]
             print(f'Best model: {best_model}, valid R2: {perf.sort_values(by="valid_r2", ascending=False)["valid_r2"].iloc[0]}')
         else:
             best_trial = perf.sort_values(by="valid_roc_auc", ascending=False)["trial"].iloc[0]
-            best_model = trials.trials[best_trial]["result"]["model"]
+            best_model = study.trials[best_trial].user_attrs["model"]
             print(f'Best model: {best_model}, valid ROC_AUC: {perf.sort_values(by="valid_roc_auc", ascending=False)["valid_roc_auc"].iloc[0]}')
 
         bmodel_prefix = "_".join(os.path.basename(best_model).split("_")[:-1])
@@ -1707,7 +1689,7 @@ def lossfn(p):
         if os.path.isfile(best_model):
             # if the model tracker is used, the model won't be saved to the result_dir
             shutil.copy2(best_model, os.path.join(self.final_dir,
-                                              f"best_{self.params.prediction_type}_{bmodel_prefix}_{self.params.model_type}_{fd}_{bmodel_uuid}.tar.gz"))
+                                                   f"best_{self.params.prediction_type}_{bmodel_prefix}_{self.params.model_type}_{fd}_{bmodel_uuid}.tar.gz"))
 
 def parse_params(param_list):
     """Parse paramters
@@ -1780,7 +1762,7 @@ def build_search(params):
     elif params.search_type == 'user_specified':
         hs = UserSpecifiedSearch(params)
     elif params.search_type == 'hyperopt':
-        hs = HyperOptSearch(params)
+        hs = OptunaSearch(params)
     else:
         print("Incorrect search type specified")
         sys.exit(1)
diff --git a/atomsci/ddm/utils/model_file_reader.py b/atomsci/ddm/utils/model_file_reader.py
index 87bfee9b..fb073869 100644
--- a/atomsci/ddm/utils/model_file_reader.py
+++ b/atomsci/ddm/utils/model_file_reader.py
@@ -91,6 +91,61 @@ def get_model_parameters(self):
         """
         return self.metadata_dict.get("model_parameters")
 
+    def get_transformer_specific_parameters(self):
+        """Returns:
+            (str): embedding specific parameters
+
+        """
+        return self.metadata_dict.get("transformer_specific")
+
+    def get_robustscaler_with_centering(self):
+        """Returns:
+            (bool): robustscaler_with_centering
+        """
+        return self.get_transformer_specific_parameters().get("robustscaler_with_centering")
+
+    def get_robustscaler_with_scaling(self):
+        """Returns:
+            (bool): robustscaler_with_scaling
+        """
+        return self.get_transformer_specific_parameters().get("robustscaler_with_scaling")
+
+    def get_robustscaler_quartile_range(self):
+        """Returns:
+            (list[float]): robustscaler_quartile_range
+        """
+        return self.get_transformer_specific_parameters().get("robustscaler_quartile_range")
+
+    def get_robustscaler_unit_variance(self):
+        """Returns:
+            (bool): robustscaler_unit_variance
+        """
+        return self.get_transformer_specific_parameters().get("robustscaler_unit_variance")
+
+    def get_powertransformer_method(self):
+        """Returns:
+            (str): powertransformer_method
+        """
+        return self.get_transformer_specific_parameters().get("powertransformer_method")
+
+    def get_powertransformer_standardize(self):
+        """Returns:
+            (bool): powertransformer_standardize
+        """
+        return self.get_transformer_specific_parameters().get("powertransformer_standardize")
+
+    def get_imputer_strategy(self):
+        """Returns:
+            (str): imputer_strategy
+        """
+        return self.get_transformer_specific_parameters().get("imputer_strategy")
+
+    def get_transformer_dataset_key_configs(self):
+        """Returns:
+            (str): transformer_dataset_key_configs
+        """
+        return self.get_transformer_specific_parameters().get("transformer_dataset_key_configs")
+
     def get_embedding_specific_parameters(self):
         """Returns:
             (str): embedding specific parameters
@@ -104,7 +159,6 @@ def get_embedding_model_uuid(self):
         """
         return self.get_embedding_specific_parameters().get("embedding_model_uuid")
 
-
     def get_embedding_model_path(self):
         """Returns:
             (str): embedding_model_path
diff --git a/atomsci/ddm/utils/model_version_utils.py b/atomsci/ddm/utils/model_version_utils.py
index a548aebc..486f1e02 100644
--- a/atomsci/ddm/utils/model_version_utils.py
+++ b/atomsci/ddm/utils/model_version_utils.py
@@ -35,7 +35,7 @@
 
 
 # ampl versions compatible groups
-comp_dict = { '1.2': 'group1', '1.3': 'group1', '1.4': 'group2', '1.5': 'group3', '1.6': 'group3', '1.7': 'group3' }
+comp_dict = { '1.2': 'group1', '1.3': 'group1', '1.4': 'group2', '1.5': 'group3', '1.6': 'group3', '1.7': 'group3', '1.8': 'group3' }
 version_pattern = re.compile(r"[\d.]+")
 
 def get_ampl_version():
diff --git a/atomsci/ddm/utils/struct_utils.py b/atomsci/ddm/utils/struct_utils.py
index 5cafe4f7..1624ee9c 100644
--- a/atomsci/ddm/utils/struct_utils.py
+++ b/atomsci/ddm/utils/struct_utils.py
@@ -7,15 +7,13 @@
 
 import re
 import numpy as np
-import molvs
 import logging
 
 from rdkit import Chem
 from rdkit.Chem import AllChem, Draw, Descriptors
 from rdkit.Chem.MolStandardize import rdMolStandardize
 
-stdizer = molvs.standardize.Standardizer(prefer_organic=True)
-uncharger = molvs.charge.Uncharger()
+uncharger = rdMolStandardize.Uncharger()
 
 
 def get_rdkit_smiles(orig_smiles, useIsomericSmiles=True):
@@ -34,13 +32,12 @@ def get_rdkit_smiles(orig_smiles, useIsomericSmiles=True):
     mol = Chem.MolFromSmiles(orig_smiles)
     if mol is None:
         return ""
-    else:
-        return Chem.MolToSmiles(mol, isomericSmiles=useIsomericSmiles)
+    return Chem.MolToSmiles(mol, isomericSmiles=useIsomericSmiles)
 
 
 def rdkit_smiles_from_smiles(orig_smiles, useIsomericSmiles=True, useCanonicalTautomers=False, workers=1):
     """Parallel version of get_rdkit_smiles. If orig_smiles is a list and workers is > 1, spawn 'workers'
-    threads to convert input SMILES strings to standardized RDKit format.
+    processes to convert input SMILES strings to standardized RDKit format.
 
     Args:
         orig_smiles (list or str): List of SMILES strings to canonicalize.
@@ -59,7 +56,7 @@ def rdkit_smiles_from_smiles(orig_smiles, useIsomericSmiles=True, useCanonicalTa
 
     if isinstance(orig_smiles, list):
         from functools import partial
-        func = partial(rdkit_smiles_from_smiles, useIsomericSmiles=useIsomericSmiles, 
+        func = partial(rdkit_smiles_from_smiles, useIsomericSmiles=useIsomericSmiles,
                        useCanonicalTautomers=useCanonicalTautomers)
         if workers > 1:
             from multiprocessing import pool
@@ -85,7 +82,7 @@ def rdkit_smiles_from_smiles(orig_smiles, useIsomericSmiles=True, useCanonicalTa
 
 def mols_from_smiles(orig_smiles, workers=1):
     """Parallel function to create RDKit Mol objects for a list of SMILES strings. If orig_smiles is a list
-    and workers is > 1, spawn 'workers' threads to convert input SMILES strings to Mol objects.
+    and workers is > 1, spawn 'workers' processes to convert input SMILES strings to Mol objects.
 
     Args:
         orig_smiles (list or str): List of SMILES strings to convert to Mol objects.
@@ -115,7 +112,7 @@ def mols_from_smiles(orig_smiles, workers=1):
     return mols
 
 
-def base_smiles_from_smiles(orig_smiles, useIsomericSmiles=True, removeCharges=False, 
+def base_smiles_from_smiles(orig_smiles, useIsomericSmiles=True, removeCharges=False,
                             useCanonicalTautomers=False, workers=1):
     """Generate standardized SMILES strings for the largest fragments of each molecule specified by
     orig_smiles. Strips salt groups and replaces any rare isotopes with the most common ones for each element.
@@ -225,9 +222,14 @@ def base_mol_from_smiles(orig_smiles, useIsomericSmiles=True, removeCharges=Fals
     cmpd_mol = Chem.MolFromSmiles(orig_smiles)
     if cmpd_mol is None:
         return None
-    std_mol = stdizer.isotope_parent(stdizer.fragment_parent(cmpd_mol), skip_standardize=True)
-    if removeCharges:
-        std_mol = uncharger(std_mol)
+    try:
+        std_mol = rdMolStandardize.IsotopeParent(
+            rdMolStandardize.FragmentParent(cmpd_mol),
+            skipStandardize=True)
+        if removeCharges:
+            std_mol = uncharger.uncharge(std_mol)
+    except Exception:
+        std_mol = None
     return std_mol
 
 
@@ -294,12 +296,17 @@ def base_mol_from_inchi(inchi_str, useIsomericSmiles=True, removeCharges=False):
     cmpd_mol = Chem.inchi.MolFromInchi(inchi_str)
     if cmpd_mol is None:
         return None
-    std_mol = stdizer.isotope_parent(stdizer.fragment_parent(cmpd_mol), skip_standardize=True)
-    if removeCharges:
-        std_mol = uncharger(std_mol)
+    try:
+        std_mol = rdMolStandardize.IsotopeParent(
+            rdMolStandardize.FragmentParent(cmpd_mol),
+            skipStandardize=True)
+        if removeCharges:
+            std_mol = uncharger.uncharge(std_mol)
+    except Exception as e:
+        print(f"Error standardizing InChI {inchi_str}: {e}")
+        std_mol = None
     return std_mol
 
-
 def draw_structure(smiles_str, image_path, image_size=500):
     """Draw structure for the compound with the given SMILES string as a PNG file.
 
@@ -418,7 +425,7 @@ def _merge_dataframes_by_smiles(dataframes, smiles_col='rdkit_smiles', id_col='c
                 new_df = new_df.drop([lCol, rCol], axis=1)
         left_df = new_df
 
-    return new_df
+    return left_df
 
 
 def smiles_to_inchi_key(smiles):
@@ -514,6 +521,4 @@ def canonical_tautomers_from_smiles(smiles):
         smiles = [smiles]
     mols = [Chem.MolFromSmiles(smi) for smi in smiles]
     canon_tautomers = [taut_enum.Canonicalize(m) if m is not None else None for m in mols]
-    return [Chem.MolToSmiles(m) if m is not None else '' for m in canon_tautomers]
-
-
+    return [Chem.MolToSmiles(m) if m is not None else '' for m in canon_tautomers]
\ No newline at end of file
diff --git a/build.sh b/build.sh
index 97b990c0..3b55d23f 100755
--- a/build.sh
+++ b/build.sh
@@ -13,4 +13,4 @@ DIST_DIR=${TOPDIR}.dist
 mkdir -p $DIST_DIR
 mkdir -p $BUILD_DIR
 
-python3 setup.py build -b $BUILD_DIR egg_info --egg-base $BUILD_DIR bdist_wheel --dist-dir $DIST_DIR  || exit 1
+uv build --wheel --out-dir "$DIST_DIR" || exit 1
\ No newline at end of file
diff --git a/install-ampl/ampl-venv.sh b/install-ampl/ampl-venv.sh
new file mode 100644
index 00000000..e179970f
--- /dev/null
+++ b/install-ampl/ampl-venv.sh
@@ -0,0 +1,27 @@
+#!/bin/bash
+
+DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
+cd $DIR
+
+# --- begin: must be sourced guard ---
+(return 0 2>/dev/null) || {
+    echo "b This script must be sourced, not executed."
+    echo "   Use:  source ${BASH_SOURCE[0]}  or  . ${BASH_SOURCE[0]}"
+    exit 1
+}
+# --- end: must be sourced guard ---
+
+PY_VER=3.10
+PY_VER_STR="${PY_VER//./}"
+
+# load system python module
+echo module load python/$PY_VER
+module load python/$PY_VER || return 1
+
+# activate the venv
+VENV=$(realpath venvs/ampl-py${PY_VER_STR}-venv/bin/activate) || return 1
+echo "Activating ampl-py${PY_VER_STR}-venv @ $VENV"
+source venvs/ampl-py${PY_VER_STR}-venv/bin/activate || return 1
+
+
+
diff --git a/install-ampl/create_venv.sh b/install-ampl/create_venv.sh
new file mode 100755
index 00000000..7c42828b
--- /dev/null
+++ b/install-ampl/create_venv.sh
@@ -0,0 +1,59 @@
+#!/bin/bash
+
+DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
+cd $DIR
+
+VENV_ID=ampl
+SYNC_PACKAGES=0
+
+PATH=~/.local/bin:$PATH
+UV_CMD=$(which uv)
+
+if [ "$UV_CMD" = "" ]; then
+  curl -LsSf https://astral.sh/uv/install.sh | sh
+fi
+
+OPTIONS=(
+  "python/3.10" "Python 3.10 " off
+  "python/3.11" "Python 3.11 [deepchem support questionable]" off
+)
+
+# Show checklist and capture selected values
+CHOICES=$(dialog --clear --title "Select Options" \
+  --checklist "Use SPACE to select/unselect, ENTER to confirm:" 20 60 10 \
+  "${OPTIONS[@]}" \
+  3>&1 1>&2 2>&3)
+
+dialog --clear \
+  --title "Sync Packages" \
+  --yesno "Do you want to sync packages in the venvs?" 8 60
+
+response=$?
+
+if [[ $response -eq 0 ]]; then
+    SYNC_PACKAGES=1
+else
+    SYNC_PACKAGES=0
+fi
+
+clear  # remove dialog UI
+
+# CHOICES is a quoted space-separated list of values, e.g. "build" "limits"
+# Remove quotes and loop
+for choice in $CHOICES; do
+    # Remove surrounding quotes
+    val="${choice%\"}"
+    val="${val#\"}"
+    module_name=$val
+    python_version="${val#*/}"
+    python_version_str="${python_version//./}"
+    venv_name="${VENV_ID}-py${python_version_str}-venv"
+    module load $module_name
+    if [ ! -e $(pwd)/venvs/$venv_name ]; then
+      uv venv --python $(which python3) $(pwd)/venvs/$venv_name
+    fi
+    if [ $SYNC_PACKAGES -eq 1 ]; then
+      ./uv-sync.sh $python_version
+    fi
+done
+
diff --git a/install-ampl/pyproject.toml b/install-ampl/pyproject.toml
new file mode 100644
index 00000000..93b2860f
--- /dev/null
+++ b/install-ampl/pyproject.toml
@@ -0,0 +1,117 @@
+
+[project]
+name = "atomsci-ampl"
+description = "ATOM Modeling PipeLine"
+version = "1.8.0"
+requires-python = ">=3.9,<3.12"
+
+# NOTE:
+# * deepchem only works with python <= 3.10 (and possibly 3.11)
+#   - seems to install in python 3.11 but website says suports only 
+#     python <= 3.10
+#   - does NOT work with python 3.12+ due to tensorflow-addons dependency
+#     which is no longer supported and only has support up to python 3.11
+# * ampl only works with python 3.9, 3.10, 3.11 (due to deepchem)
+
+# Dependencies for all Python versions
+dependencies = [
+    "torch-geometric",
+    "xgboost",
+    "umap-learn",
+    "imbalanced-learn",
+    "ipykernel",
+    "ipython",
+    "pytest",
+    "seaborn",
+    "matplotcheck",
+    "numpy<2",
+    "atomsci-clients",
+
+    # deepchem 2.7.1 for Python 3.9 version
+    "deepchem[torch,tensorflow]==2.7.1; python_version < '3.10'",
+    # deepchem 2.8.0 deps [NOTE: may not work on python 3.11]
+    "deepchem[torch,tensorflow]==2.8.0; python_version >= '3.10'",
+
+    # deepchem[tensorflow] deps
+    "tensorflow-cpu==2.14.0",
+
+    "rdkit>=2024.3.5; python_version >= '3.10'",
+    "dgl==2.1.0; python_version >= '3.10'",
+
+    "pyg_lib==0.2.0; python_version < '3.10'",
+    "pyg_lib==0.3.1; python_version >= '3.10'",
+
+    "torch==2.0.1+cpu; python_version < '3.10'",
+    "torch==2.1.2+cpu; python_version >= '3.10'",
+    "torch_cluster",
+    "torch_scatter",
+    "torch_sparse",
+    "torch_spline_conv",
+]
+
+[build-system]
+requires = ["setuptools>=61.0", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[tool.setuptools.packages.find]
+include = ["atomsci*"]
+namespaces = true
+
+[tool.uv]
+managed = true
+allow-insecure-host = [
+    "download.pytorch.org",
+    "data.dgl.ai",
+    "data.pyg.org"
+]
+
+[[tool.uv.index]]
+name = "pytorch"
+url = "https://download.pytorch.org/whl/cpu/torch_stable.html"
+format = "flat"
+
+[[tool.uv.index]]
+name = "dgl"
+url = "https://data.dgl.ai/wheels/repo.html"
+format = "flat"
+
+[[tool.uv.index]]
+name = "pyg_lib_py39"
+url = "https://data.pyg.org/whl/torch-2.0.1+cpu.html"
+format = "flat"
+
+[[tool.uv.index]]
+name = "pyg_lib_py310"
+url = "https://data.pyg.org/whl/torch-2.1.2+cpu.html"
+format = "flat"
+
+[tool.uv.sources]
+# code repos
+atomsci-clients = { git = "ssh://git@czgitlab.llnl.gov:7999/atom/clients.git", rev = "master" }
+
+# dgl
+dgl = { index = "dgl" }
+
+# torch
+torch = { index = "pytorch" }
+
+pyg_lib = [
+    { index = "pyg_lib_py39", marker = "python_version < '3.10'" },
+    { index = "pyg_lib_py310", marker = "python_version >= '3.10'" },
+]
+torch_cluster = [
+    { index = "pyg_lib_py39", marker = "python_version < '3.10'" },
+    { index = "pyg_lib_py310", marker = "python_version >= '3.10'" },
+]
+torch_scatter = [
+    { index = "pyg_lib_py39", marker = "python_version < '3.10'" },
+    { index = "pyg_lib_py310", marker = "python_version >= '3.10'" },
+]
+torch_sparse = [
+    { index = "pyg_lib_py39", marker = "python_version < '3.10'" },
+    { index = "pyg_lib_py310", marker = "python_version >= '3.10'" },
+]
+torch_spline_conv = [
+    { index = "pyg_lib_py39", marker = "python_version < '3.10'" },
+    { index = "pyg_lib_py310", marker = "python_version >= '3.10'" },
+]
diff --git a/install-ampl/uv-sync.sh b/install-ampl/uv-sync.sh
new file mode 100755
index 00000000..d982831c
--- /dev/null
+++ b/install-ampl/uv-sync.sh
@@ -0,0 +1,15 @@
+DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
+cd $DIR
+
+PATH=~/.local/bin:$PATH
+UV_CMD=$(which uv)
+
+if [ "$UV_CMD" = "" ]; then
+  curl -LsSf https://astral.sh/uv/install.sh | sh
+fi
+
+. ampl-venv.sh || exit 1
+
+# NOTE add --refresh to get latest version of repos
+uv sync --active
+
diff --git a/install.sh b/install.sh
index 22d13b29..b492cc4c 100755
--- a/install.sh
+++ b/install.sh
@@ -18,4 +18,4 @@ DIST_DIR=${TOPDIR}.dist
 
 mkdir -p $DIST_DIR
 
-python3 -m pip install --pre --upgrade --no-index --find-links=$DIST_DIR --no-deps ${PACKAGE}_${APP} $INSTALL --force-reinstall -I -v || exit 1
+uv pip install --pre --upgrade --no-index --find-links="$DIST_DIR" --no-deps ${PACKAGE}_${APP} --reinstall -v || exit 1
\ No newline at end of file
diff --git a/install_dev.sh b/install_dev.sh
index 3a0b8c32..51ffcf53 100755
--- a/install_dev.sh
+++ b/install_dev.sh
@@ -3,5 +3,5 @@
 DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
 cd $DIR
 
-pip install -e . --user
+uv pip install -e .
 
diff --git a/pip/cpu_requirements.txt b/pip/cpu_requirements.txt
index ff87562f..4e0db5f5 100644
--- a/pip/cpu_requirements.txt
+++ b/pip/cpu_requirements.txt
@@ -1,48 +1,103 @@
-# Minimal pip requirement list for AMPL with CPU support only. This includes only the packages directly imported by AMPL code, plus a few others commonly used during development.
--i https://download.pytorch.org/whl/cpu
---find-links https://data.dgl.ai/wheels/repo.html
---extra-index-url  https://pypi.python.org/simple
-# for LC developers: comment out pypi index url and use t$
+# ================================
+# Indexes and extra repositories
+# ================================
+-i https://pypi.org/simple
+--extra-index-url https://download.pytorch.org/whl/cpu
+
+# For LC developers: comment out PyPI index url and use internal repo instead
+# -i https://wci-repo.llnl.gov/repository/pypi/simple
 # --extra-index-url https://wci-repo.llnl.gov/repository/$
+# or define variables to get the CERT
+# $ export REQUESTS_CA_BUNDLE=/etc/pki/tls/cert.pem
+# $ export SSL_CERT_FILE=/etc/pki/tls/cert.pem
+
+# DGL wheels (generic and torch-version specific)
+--find-links https://data.dgl.ai/wheels/repo.html
+--find-links https://data.dgl.ai/wheels/torch-2.1/repo.html
 
+# =============================
+# Core deep learning frameworks
+# =============================
+
+# PyTorch CPU; Python 3.10 compatible; aligned with DGL torch-2.1 wheels
+torch==2.1.2+cpu
+torchdata==0.7.1
+
+# Lightning; pin to a recent stable series to avoid breaking changes
+lightning==2.2.5
+
+# TensorFlow CPU; compatible with DeepChem 2.8
 tensorflow-cpu~=2.14.0
 
-torch==2.0.1
-torch_geometric
-lightning
 
-dgl==1.1.2
-dgllife
+# ==========================
+# Graph / GNN related stack
+# ==========================
+
+# DGL stack; 2.1.0 has matching wheels for torch 2.1.x
+dgl==2.1.0
+dgllife==0.3.2
+
+# PyTorch Geometric; works with torch 2.1.x on CPU
+torch_geometric==2.7.0
+
+# DGL currently imports pydantic but does not declare it.
+# Stay on pydantic 1.x for maximum compatibility.
+pydantic>=1.10,<2
+
+
+# ==================
+# Numeric / ML stack
+# ==================
+
+# Constrained for broad compatibility (esp. with DeepChem, scikit-learn)
+numpy<2
+scipy>=1.10
 
 scikit-learn==1.2.2
-hyperopt
+optuna
 
 xgboost
+lightgbm
+
+umap-learn
+
+
+# ============================
+# Visualization and plotting
+# ============================
 
 bokeh
 matplotlib
 matplotlib-venn
 seaborn>=0.13.0
-umap-learn
 
-pyarrow
 
-bravado
+# =========================
+# Data / IO / misc tooling
+# =========================
 
-imblearn
+pyarrow
+pyyaml>=6.0
+bravado
 
-# optional for home users: prettier images in RDKit
-# requires pkg-config to build: sudo apt-get pkg-config
-# requires Cairo: sudo apt-get install libcairo2-dev
-# pycairo
+# imbalanced-learn is the canonical package name on PyPI
+imbalanced-learn
 
 maestrowf
-MolVS
-mordred
 
-pytest
-ipykernel
 
-deepchem==2.7.1
+# ==========================
+# Cheminformatics / domain
+# ==========================
+
+deepchem==2.8
 rdkit==2024.3.5
-pyyaml==5.4.1
+mordred
+
+# ==========================
+# Dev / runtime utilities
+# ==========================
+
+pytest
+ipykernel
\ No newline at end of file
diff --git a/pip/cuda_requirements.txt b/pip/cuda_requirements.txt
index f805072f..c8b753db 100644
--- a/pip/cuda_requirements.txt
+++ b/pip/cuda_requirements.txt
@@ -17,7 +17,7 @@ dgl==1.1.2
 dgllife
 
 scikit-learn==1.2.2
-hyperopt
+optuna
 
 xgboost
 
@@ -39,7 +39,6 @@ imblearn
 # pycairo
 
 maestrowf
-MolVS
 mordred
 
 pytest
@@ -48,4 +47,4 @@ ipykernel
 deepchem==2.7.1
 rdkit==2024.3.5
 
-pyyaml==5.4.1
+pyyaml>=6.0
diff --git a/pip/docker_requirements.txt b/pip/docker_requirements.txt
index 6d1c3d24..24d9de2e 100644
--- a/pip/docker_requirements.txt
+++ b/pip/docker_requirements.txt
@@ -16,7 +16,7 @@ dgllife
 
 networkx==2.8.8
 scikit-learn==1.2.2
-hyperopt
+optuna
 
 xgboost
 
@@ -36,7 +36,6 @@ bravado
 # pycairo
 
 maestrowf
-MolVS
 mordred
 pytest
 ipykernel
@@ -45,4 +44,4 @@ jupyterlab
 
 deepchem==2.7.1
 rdkit==2024.3.5
-pyyaml==5.4.1
+pyyaml>=6.0
diff --git a/pip/mchip_requirements.txt b/pip/mchip_requirements.txt
index 5fe6dd20..7b44d6fa 100644
--- a/pip/mchip_requirements.txt
+++ b/pip/mchip_requirements.txt
@@ -1,4 +1,4 @@
-# Minimal pip requirement list for AMPL with CPU support only for MacOS ARM chip. This includes only the packages directly imported by AMPL code, plus a few others commonly used during development. 
+# Minimal pip requirement list for AMPL with CPU support only for MacOS ARM chip. This includes only the packages directly imported by AMPL code, plus a few others commonly used during development.
 -i https://download.pytorch.org/whl/cpu
 --find-links https://data.dgl.ai/wheels/repo.html
 --extra-index-url  https://pypi.python.org/simple
@@ -16,7 +16,7 @@ dgl==1.1.2
 dgllife
 
 scikit-learn==1.2.2
-hyperopt
+optuna
 
 xgboost
 
@@ -35,10 +35,9 @@ imblearn
 # optional for home users: prettier images in RDKit
 # requires pkg-config to build: sudo apt-get pkg-config
 # requires Cairo: sudo apt-get install libcairo2-dev
-# pycairo 
+# pycairo
 
 maestrowf
-MolVS
 mordred
 
 pytest
@@ -48,4 +47,4 @@ jupyterlab
 
 deepchem==2.7.1
 rdkit==2024.3.5
-pyyaml==5.4.1
+pyyaml>=6.0
diff --git a/pip/readthedocs_requirements.txt b/pip/readthedocs_requirements.txt
index 02f83b92..46900873 100644
--- a/pip/readthedocs_requirements.txt
+++ b/pip/readthedocs_requirements.txt
@@ -1,10 +1,9 @@
 bravado
 deepchem
-hyperopt
+optuna
 IPython
 joblib
 matplotlib
-molvs
 numpy
 pandas
 pygments
diff --git a/pip/rocm_requirements.txt b/pip/rocm_requirements.txt
index 08601156..78f1ac63 100644
--- a/pip/rocm_requirements.txt
+++ b/pip/rocm_requirements.txt
@@ -1,4 +1,4 @@
-# Minimal pip requirement list for AMPL with ROCm-enabled AMD GPU's. This includes only the packages directly imported by AMPL code, plus a few others commonly used during development. It requires installation of ROCm first. 
+# Minimal pip requirement list for AMPL with ROCm-enabled AMD GPU's. This includes only the packages directly imported by AMPL code, plus a few others commonly used during development. It requires installation of ROCm first.
 
 ### amd gpu: https://blog.tensorflow.org/2022/09/announcing-tensorflow-official-build-collaborators.html
 tensorflow
@@ -27,7 +27,6 @@ imblearn
 
 
 maestrowf
-MolVS
 mordred
 
 pytest
@@ -36,4 +35,4 @@ ipykernel
 deepchem[tensorflow] # instructions unclear here - trouble on my home system
 deepchem[torch]      # instructions unclear here - works fine on home system
 
-pyyaml==5.4.1
+pyyaml>=6.0
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 00000000..fdf570bf
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,138 @@
+[project]
+name = "atomsci-ampl"
+dynamic = ["version"]
+description = "ATOMSCI AMPL is an open-source Python package for molecular property prediction in drug discovery."
+requires-python = ">=3.10,<3.11"
+readme = "README.md"
+license = "MIT"
+license-files = ["LICENSE"]
+dependencies = [
+  "lightning==2.2.5",
+  "pydantic>=1.10,<2",
+  "numpy<2",
+  "scipy>=1.10",
+  "scikit-learn==1.2.2",
+  "optuna",
+  "xgboost",
+  "lightgbm",
+  "umap-learn",
+  "bokeh",
+  "matplotlib",
+  "matplotlib-venn",
+  "seaborn>=0.13.0",
+  "pyarrow",
+  "pyyaml>=6.0",
+  "bravado",
+  "imbalanced-learn",
+  "imblearn",
+  "maestrowf",
+  "deepchem==2.8",
+  "rdkit==2024.3.5",
+  "mordred",
+  "setuptools",
+]
+
+[project.urls]
+Homepage = "https://github.com/ATOMScience-org/AMPL"
+Documentation = "https://ampl.readthedocs.io/en/latest/"
+Repository = "https://github.com/ATOMScience-org/AMPL"
+Source = "https://github.com/ATOMScience-org/AMPL"
+
+# local developer tools
+[dependency-groups]
+dev = [
+  "ipykernel",
+  "jupyter",
+  "jupyterlab",
+  "pytest",
+  "pytest-cov",
+  "pytest-mock",
+  "pytest-xdist",
+  "matplotcheck",
+  "ruff",
+]
+
+docker = [
+  "networkx==2.8.8",
+]
+
+[project.optional-dependencies]
+# RTD-installable docs deps
+docs = [
+  "ipython",
+  "joblib",
+  "pandas",
+  "pygments",
+  "sphinx",
+  "sphinx_rtd_theme",
+]
+
+# hardware/runtime variants
+cpu = [
+  "torch==2.1.2",
+  "torchdata==0.7.1",
+  "tensorflow-cpu~=2.14.0; platform_system == 'Linux'",
+  "tensorflow~=2.14.0; platform_system == 'Darwin'",
+  "dgl==2.1.0",
+  "dgllife==0.3.2",
+  "torch-geometric==2.7.0",
+]
+
+cuda = [
+  "torch==2.1.2",
+  "torchdata==0.7.1",
+  "tensorflow~=2.14.0",
+  "dgl==2.1.0",
+  "dgllife==0.3.2",
+  "torch-geometric==2.7.0",
+]
+
+rocm = [
+  "torch==2.1.2",
+  "torchdata==0.7.1",
+  "tensorflow~=2.14.0",
+  "dgl==2.1.0",
+  "dgllife==0.3.2",
+  "torch-geometric==2.7.0",
+]
+
+mchip = [
+  "torch==2.1.2",
+  "torchdata==0.7.1",
+  "tensorflow-macos~=2.14.0",
+  "tensorflow-metal~=1.1.0",
+  "dgl==2.1.0",
+  "dgllife==0.3.2",
+  "torch-geometric==2.7.0",
+]
+
+[build-system]
+requires = ["hatchling>=1.24.0"]
+build-backend = "hatchling.build"
+
+[tool.hatch.version]
+path = "VERSION"
+pattern = "(?P<version>\\d+\\.\\d+\\.\\d+.*)"
+
+[tool.hatch.build.targets.wheel]
+packages = ["atomsci"]
+exclude = [
+  "atomsci/ddm/examples/archive/**",        # old tutorials
+  "atomsci/ddm/test/test_datasets/**",      # test datasets
+  "**/*.tar.gz",
+  "**/*.ipynb",
+  "**/*.rst",
+]
+
+[tool.hatch.build.targets.sdist]
+exclude = [
+  "atomsci/ddm/examples/archive/**",        # old tutorials
+  "atomsci/ddm/test/test_datasets/**",      # test datasets
+  "**/*.tar.gz",
+  "**/*.ipynb",
+  "**/*.rst",
+]
+
+[tool.pytest.ini_options]
+testpaths = ["atomsci/ddm/test"]
+addopts = "-ra"
diff --git a/setup.py b/setup.py
deleted file mode 100644
index 7fc14b74..00000000
--- a/setup.py
+++ /dev/null
@@ -1,35 +0,0 @@
-#
-# setup.py
-#
-
-from setuptools import setup, find_packages
-import os
-import glob
-
-PACKAGE = 'atomsci'
-
-here = os.path.abspath(os.path.dirname(__file__))
-parent_dir = os.path.abspath(os.path.join(here, os.pardir))
-script_files = glob.glob("scripts/*")
-
-setup(
-    name='{}-ampl'.format(PACKAGE),
-    namespace_packages=[PACKAGE],
-    # install_requires=['distribute'],
-    include_package_data=True,
-    version=open('VERSION').read().strip(),
-    description='{} AMPL Python Package'.format(PACKAGE),
-    zip_safe=False,
-    data_files=[],
-    packages=find_packages(),
-    scripts=script_files,
-    install_requires=[
-    ],
-    entry_points={
-#        'console_scripts': [
-#            ("{pkg}_glo_command = "
-#             "{pkg}.glo:glo_command_main"
-#             .format(pkg=PACKAGE)),
-#        ],
-    },
-)
diff --git a/sync_uv_env.sh b/sync_uv_env.sh
new file mode 100755
index 00000000..7d4311bc
--- /dev/null
+++ b/sync_uv_env.sh
@@ -0,0 +1,78 @@
+#!/usr/bin/env bash
+# Purpose:
+#    syncs the environment, does not save a platform lockfile
+#
+# Use when:
+#    want to create/update a local env from existing lock/deps. build an env to run.
+#
+# Usage:
+#    ./sync_uv_env.sh <cpu|cuda|rocm|mchip>
+
+usage() {
+  echo "Usage: $0 <cpu|cuda|rocm|mchip>"
+}
+
+platform="${1:-}"
+[[ -n "$platform" ]] || { usage; exit 1; }
+
+case "$platform" in
+  cpu)
+    extra="cpu"
+    torch_index="https://download.pytorch.org/whl/cpu"
+    bootstrap_torch=true
+    ;;
+  cuda)
+    extra="cuda"
+    torch_index="https://download.pytorch.org/whl/cu121"
+    bootstrap_torch=true
+    ;;
+  rocm)
+    extra="rocm"
+    torch_index="https://download.pytorch.org/whl/rocm5.6"
+    bootstrap_torch=true
+    ;;
+  mchip)
+    extra="mchip"
+    torch_index=""
+    bootstrap_torch=false
+    ;;
+  *)
+    echo "Invalid platform: $platform"
+    usage
+    exit 1
+    ;;
+esac
+
+venv_dir=".venv-${platform}"
+lockfile="uv.lock.${platform}"
+temp_lock="uv.lock"
+
+[[ -f "$lockfile" ]] || {
+  echo "Missing lockfile: $lockfile"
+  echo "Run ./update_uv_lock.sh $platform first"
+  exit 1
+}
+
+cleanup() {
+  rm -f "$temp_lock"
+}
+trap cleanup EXIT
+
+cp "$lockfile" "$temp_lock"
+rm -rf "$venv_dir"
+
+uv venv --python 3.10 "$venv_dir"
+
+if [[ "$bootstrap_torch" == true ]]; then
+  uv pip install --python "$venv_dir/bin/python" \
+    --index-url "$torch_index" \
+    torch==2.1.2 torchdata==0.7.1
+fi
+
+UV_PROJECT_ENVIRONMENT="$venv_dir" \
+  uv sync --python "$venv_dir/bin/python" --group dev --extra "$extra" --locked
+
+uv pip install --python "$venv_dir/bin/python" -e . --no-deps
+
+echo "Refreshed $venv_dir from $lockfile"
+echo "Activate with: source $venv_dir/bin/activate"
\ No newline at end of file
diff --git a/update_uv_lock.sh b/update_uv_lock.sh
new file mode 100755
index 00000000..64ad9aa2
--- /dev/null
+++ b/update_uv_lock.sh
@@ -0,0 +1,75 @@
+#!/usr/bin/env bash
+#
+# Purpose:
+#     regenerates uv.lock.<platform>
+#     generates the righ lockfile for the specified platform
+#
+# Use when:
+#     dependencies changed, pyproject.toml changed, or lockfile mismatch happens. refresh the commited lockfile.
+#
+# Usage:
+#     ./update_uv_lock.sh <cpu|cuda|rocm|mchip>
+
+set -euo pipefail
+
+platform="${1:-}"
+venv_dir=".venv-${platform}"
+temp_lock="uv.lock"
+
+usage() {
+  echo "Usage: $0 <cpu|cuda|rocm|mchip>"
+}
+
+case "$platform" in
+  cpu)
+    extra="cpu"
+    torch_index="https://download.pytorch.org/whl/cpu"
+    bootstrap_torch="yes"
+    ;;
+  cuda)
+    extra="cuda"
+    torch_index="https://download.pytorch.org/whl/cu121"
+    bootstrap_torch="yes"
+    ;;
+  rocm)
+    extra="rocm"
+    torch_index="https://download.pytorch.org/whl/rocm5.6"
+    bootstrap_torch="yes"
+    ;;
+  mchip)
+    extra="mchip"
+    torch_index=""
+    bootstrap_torch="no"
+    ;;
+  "")
+    usage
+    exit 1
+    ;;
+  *)
+    echo "Invalid platform: $platform"
+    usage
+    exit 1
+    ;;
+esac
+
+lockfile="uv.lock.${platform}"
+
+cleanup() {
+  rm -f "$temp_lock"
+}
+trap cleanup EXIT
+
+rm -rf "$venv_dir"
+uv venv --python 3.10 "$venv_dir"
+
+if [[ "$bootstrap_torch" == "yes" ]]; then
+  uv pip install --python "$venv_dir/bin/python" \
+    --index-url "$torch_index" \
+    torch==2.1.2 torchdata==0.7.1
+fi
+
+UV_PROJECT_ENVIRONMENT="$venv_dir" \
+  uv sync --python "$venv_dir/bin/python" --extra "$extra" --group dev
+
+cp "$temp_lock" "$lockfile"
+echo "Updated $lockfile"
\ No newline at end of file
diff --git a/uv.lock.cpu b/uv.lock.cpu
new file mode 100644
index 00000000..06be863c
--- /dev/null
+++ b/uv.lock.cpu
@@ -0,0 +1,3859 @@
+version = 1
+revision = 3
+requires-python = "==3.10.*"
+
+[[package]]
+name = "absl-py"
+version = "2.4.0"
+source = { registry = "https://pypi.org/simple" }
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+wheels = [
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+]
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+version = "2.4.0"
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+[[package]]
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+    { name = "multidict" },
+    { name = "propcache" },
+    { name = "yarl" },
+]
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